From 915795d14596eed70fd32eccc5865b677d2f4cd0 Mon Sep 17 00:00:00 2001 From: Mohammed Ali Date: Fri, 16 Jan 2026 18:08:23 +0200 Subject: [PATCH 01/10] Increment version number to 2.2.1.9000 --- DESCRIPTION | 2 +- NEWS.md | 2 ++ 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index 9799a187..a22b7229 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,6 +1,6 @@ Package: dbparser Title: Drugs Databases Parser -Version: 2.2.1 +Version: 2.2.1.9000 Authors@R: c( person("Mohammed", "Ali", email = "moh_fcis@yahoo.com", role = c("aut", "cre")), diff --git a/NEWS.md b/NEWS.md index ab0867fa..d2965d49 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,3 +1,5 @@ +# dbparser (development version) + # dbparser # dbparser 2.2.1 From b919218dc3c43bdb031c6d3ecd032236c8e1c9e0 Mon Sep 17 00:00:00 2001 From: Mohammed Ali Date: Thu, 22 Jan 2026 16:47:34 +0200 Subject: [PATCH 02/10] - Updated JOSS paper --- paper.bib | 201 ++++++++++++++++++++++++++++++++++++++++++++---------- paper.md | 177 +++++++++++++++++++++++++++++++---------------- 2 files changed, 284 insertions(+), 94 deletions(-) diff --git a/paper.bib b/paper.bib index 035d511a..c6d70853 100644 --- a/paper.bib +++ b/paper.bib @@ -1,8 +1,8 @@ ```bibtex @article{wishart2018drugbank, - title={DrugBank 5.0: a major update to the DrugBank database for 2018}, + title={{DrugBank 5.0}: a major update to the {DrugBank} database for 2018}, author={Wishart, David S and Feunang, Yannick D and Guo, An C and Lo, Elvis J and Marcu, Ana and Grant, Jason R and Sajed, Tanvir and Johnson, Daniel and Li, Carin and Sayeeda, Zinat and others}, - journal={Nucleic acids research}, + journal={Nucleic Acids Research}, volume={46}, number={D1}, pages={D1074--D1082}, @@ -14,17 +14,17 @@ @article{wishart2018drugbank @article{tatonetti2012data, title={Data-driven prediction of drug effects and interactions}, author={Tatonetti, Nicholas P and Ye, Patrick P and Daneshjou, Roxana and Altman, Russ B}, - journal={Science translational medicine}, + journal={Science Translational Medicine}, volume={4}, number={125}, - pages={125ra31--125ra31}, + pages={125ra31}, year={2012}, publisher={American Association for the Advancement of Science}, doi={10.1126/scitranslmed.3003377} } @article{galeano2022onsides, - title={OnSIDES: A Database of Drug Side Effects Derived from FDA Structured Product Labels}, + title={{OnSIDES}: A database of drug side effects derived from {FDA} structured product labels}, author={Galeano, Diego and Li, Sheng and Gerber, Michael and Tatonetti, Nicholas P}, journal={medRxiv}, year={2022}, @@ -32,10 +32,22 @@ @article{galeano2022onsides doi={10.1101/2022.05.18.22275166} } +@article{kuhn2016sider, + title={The {SIDER} database of drugs and side effects}, + author={Kuhn, Michael and Letunic, Ivica and Jensen, Lars Juhl and Bork, Peer}, + journal={Nucleic Acids Research}, + volume={44}, + number={D1}, + pages={D1075--D1079}, + year={2016}, + publisher={Oxford University Press}, + doi={10.1093/nar/gkv1075} +} + @article{wickham2014tidy, title={Tidy data}, author={Wickham, Hadley}, - journal={Journal of statistical software}, + journal={Journal of Statistical Software}, volume={59}, number={10}, pages={1--23}, @@ -43,10 +55,21 @@ @article{wickham2014tidy doi={10.18637/jss.v059.i10} } +@article{wickham2019welcome, + title={Welcome to the Tidyverse}, + author={Wickham, Hadley and Averick, Mara and Bryan, Jennifer and Chang, Winston and McGowan, Lucy D'Agostino and Fran{\c{c}}ois, Romain and Grolemund, Garrett and Hayes, Alex and Henry, Lionel and Hester, Jim and others}, + journal={Journal of Open Source Software}, + volume={4}, + number={43}, + pages={1686}, + year={2019}, + doi={10.21105/joss.01686} +} + @article{gentleman2004bioconductor, - title={Bioconductor: open software development for computational biology and bioinformatics}, + title={{Bioconductor}: open software development for computational biology and bioinformatics}, author={Gentleman, Robert C and Carey, Vincent J and Bates, Douglas M and Bolstad, Ben and Dettling, Marcel and Dudoit, Sandrine and Ellis, Byron and Gautier, Laurent and Ge, Yongchao and Gentry, Jeff and others}, - journal={Genome biology}, + journal={Genome Biology}, volume={5}, number={10}, pages={R80}, @@ -55,35 +78,120 @@ @article{gentleman2004bioconductor doi={10.1186/gb-2004-5-10-r80} } -@manual{dowle2023datatable, - title={data.table: Extension of `data.frame`}, - author={Dowle, Matt and Srinivasan, Arun}, +@article{parolo2023single, + title={Single-cell-led drug repurposing for {Alzheimer's} disease}, + author={Parolo, Silvia and Mariotti, Federica and Bora, Pranami and Carboni, Lucia and Domenici, Enrico}, + journal={Scientific Reports}, + volume={13}, + pages={8497}, year={2023}, - note={R package version 1.14.8}, - url={https://CRAN.R-project.org/package=data.table} + publisher={Nature Publishing Group}, + doi={10.1038/s41598-023-35621-w} } -@manual{wickham2023xml2, - title={xml2: Parse XML}, - author={Wickham, Hadley and Hester, Jim and Ooms, Jeroen}, - year={2023}, - note={R package version 1.3.5}, - url={https://CRAN.R-project.org/package=xml2} +@article{perez2021covid, + title={A {COVID-19} drug repurposing strategy through quantitative homological similarities using a topological data analysis-based framework}, + author={P{\'e}rez-Moraga, Raquel and For{\'e}s-Martos, Jaume and Suay-Garc{\'i}a, Beatriz and Duval, Jean-Louis and Falc{\'o}, Antonio and Climent, Joan}, + journal={Pharmaceutics}, + volume={13}, + number={4}, + pages={488}, + year={2021}, + publisher={MDPI}, + doi={10.3390/pharmaceutics13040488} } -@article{wickham2011testthat, - title={testthat: Get started with testing}, - author={Wickham, Hadley}, - journal={The R Journal}, - volume={3}, - number={1}, - pages={5--10}, - year={2011}, - doi={10.32614/RJ-2011-002} +@article{schubert2022transcriptome, + title={Transcriptome-guided identification of drugs for repurposing to treat age-related hearing loss}, + author={Schubert, Natalie MA and van Tuinen, Marieke and Pyott, Sonja J}, + journal={Biomolecules}, + volume={12}, + number={11}, + pages={1633}, + year={2022}, + publisher={MDPI}, + doi={10.3390/biom12111633} +} + +@article{mercatelli2022detection, + title={Detection of pan-cancer surface protein biomarkers via a network-based approach on transcriptomics data}, + author={Mercatelli, Daniele and Cabrelle, Chiara and Veltri, Pierangelo and Giorgi, Federico M}, + journal={Briefings in Bioinformatics}, + volume={23}, + number={6}, + pages={bbac400}, + year={2022}, + publisher={Oxford University Press}, + doi={10.1093/bib/bbac400} +} + +@article{yang2021mapping, + title={Mapping the landscape of synthetic lethal interactions in liver cancer}, + author={Yang, Chao and Guo, Yanru and Qian, Rui and Huang, Yuqing and Zhang, Li and Hu, Yun and others}, + journal={Pharmacological Research}, + volume={166}, + pages={105481}, + year={2021}, + publisher={Elsevier}, + doi={10.1016/j.phrs.2021.105481} +} + +@article{su2024multiancestry, + title={Multi-ancestry proteome-phenome-wide {Mendelian} randomization offers a comprehensive protein-disease atlas and potential therapeutic targets}, + author={Su, Chen-Yang and van der Graaf, Adriaan and Zhang, Wei and Jang, Dong-Keun and Kavousi, Mohsen and Sijbrands, Eric JG and Ikram, M Arfan and Voortman, Trudy}, + journal={medRxiv}, + year={2024}, + publisher={Cold Spring Harbor Laboratory Press}, + doi={10.1101/2024.01.15.24301159} +} + +@article{rischke2023machine, + title={Machine learning identifies right index finger tenderness as key signal of {DAS28-CRP} based psoriatic arthritis activity}, + author={Rischke, Sarah and Poor, Susanne Maria and Gurke, Robert and Hahnefeld, Lisa and K{\"o}hm, Michaela and Behrens, Frank and Geisslinger, Gerd and Schiffmann, Susanne}, + journal={Scientific Reports}, + volume={13}, + pages={10965}, + year={2023}, + publisher={Nature Publishing Group}, + doi={10.1038/s41598-023-37911-w} +} + +@article{namiot2023international, + title={The international clinical trials registry platform ({ICTRP}): data integrity and the trends in clinical trials, diseases, and drugs}, + author={Namiot, Evgeny D and Smirnovov{\'a}, Dagmar and Sokolov, Andrey V and Kel, Alexander E}, + journal={Frontiers in Pharmacology}, + volume={14}, + pages={1106591}, + year={2023}, + publisher={Frontiers Media SA}, + doi={10.3389/fphar.2023.1106591} +} + +@article{hammoud2020multipath, + title={{Multipath}: an {R} package to generate integrated reproducible pathway models}, + author={Hammoud, Zaynab and Kramer, Frank}, + journal={Biology}, + volume={9}, + number={12}, + pages={483}, + year={2020}, + publisher={MDPI}, + doi={10.3390/biology9120483} +} + +@article{hammoud2025multipath2, + title={{Multipath2.0}: Extending multilayer reproducible pathway models with omics data}, + author={Hammoud, Zaynab and Al Maaz, Mhaned and D'Angelo, Antonia and Kramer, Frank}, + journal={Computer Methods and Programs in Biomedicine}, + volume={244}, + pages={107958}, + year={2025}, + publisher={Elsevier}, + doi={10.1016/j.cmpb.2023.107958} } @article{garcia2012anticoagulant, - title={Parenteral anticoagulants: antithrombotic therapy and prevention of thrombosis: American College of Chest Physicians evidence-based clinical practice guidelines}, + title={Parenteral anticoagulants: antithrombotic therapy and prevention of thrombosis: {American College of Chest Physicians} evidence-based clinical practice guidelines}, author={Garcia, David A and Baglin, Trevor P and Weitz, Jeffrey I and Samama, Meyer Michel}, journal={Chest}, volume={141}, @@ -94,12 +202,35 @@ @article{garcia2012anticoagulant doi={10.1378/chest.11-2291} } -@manual{r2024, - title={R: A Language and Environment for Statistical Computing}, - author={{R Core Team}}, - organization={R Foundation for Statistical Computing}, - address={Vienna, Austria}, +@manual{dowle2023datatable, + title={{data.table}: Extension of `data.frame`}, + author={Dowle, Matt and Srinivasan, Arun}, + year={2023}, + note={R package version 1.14.8}, + url={https://CRAN.R-project.org/package=data.table} +} + +@manual{wickham2023xml2, + title={{xml2}: Parse {XML}}, + author={Wickham, Hadley and Hester, Jim and Ooms, Jeroen}, + year={2023}, + note={R package version 1.3.5}, + url={https://CRAN.R-project.org/package=xml2} +} + +@manual{dbdataset, + title={{dbdataset}: {DrugBank} dataset files in {R} dataframes}, + author={Ali, Mohammed}, year={2024}, - url={https://www.R-project.org/} + note={R package version 5.1.11}, + url={https://github.com/interstellar-egypt/dbdataset} +} + +@manual{covid19dbcand, + title={{covid19dbcand}: {Covid 19 DrugBank} selected possible drugs}, + author={Ali, Mohammed}, + year={2022}, + note={R package version 0.1.1}, + url={https://github.com/interstellar-egypt/covid19dbcand} } ``` diff --git a/paper.md b/paper.md index 0093c3e3..af7f71d6 100644 --- a/paper.md +++ b/paper.md @@ -13,127 +13,186 @@ authors: orcid: 0000-0001-8907-2374 corresponding: true affiliation: 1 + - name: Ali Ezzat + affiliation: 1 affiliations: - - name: Data Analysis Engineer at Interstellar, Egypt + - name: Independent Researcher index: 1 -date: 27 December 2025 +date: 19 January 2026 bibliography: paper.bib --- # Summary -`dbparser` is an rOpenSci peer-reviewed R package that provides a unified framework for parsing and integrating major pharmacological and pharmacovigilance databases into standardized, analysis-ready R objects. The package currently supports three essential drug information resources: DrugBank [@wishart2018drugbank], OnSIDES [@galeano2022onsides] and TWOSIDES [@tatonetti2012data]. Each database is parsed into a consistent nested list structure called a `dvobject`, which preserves complex relational hierarchies while enabling seamless integration across databases. By providing high-performance parsing functions, chainable merge operations, and comprehensive metadata tracking, `dbparser` eliminates a significant bottleneck in computational pharmacology research and enables reproducible, large-scale drug safety analyses that would otherwise require substantial custom development effort. +`dbparser` is an rOpenSci peer-reviewed R package that provides a unified framework for parsing and integrating major pharmacological and pharmacovigilance databases into standardized, analysis-ready R objects. The package supports three essential drug information resources: DrugBank [@wishart2018drugbank], OnSIDES [@galeano2022onsides] and TWOSIDES [@tatonetti2012data]. Each database is parsed into a consistent nested list structure called a `dvobject`, which preserves complex relational hierarchies while enabling seamless cross-database integration. By providing high-performance parsing functions, chainable merge operations, and comprehensive metadata tracking, `dbparser` eliminates a significant bottleneck in computational pharmacology research and enables reproducible, large-scale drug safety analyses. # Statement of Need -Pharmacological research increasingly relies on the integration of heterogeneous data sources to understand drug mechanisms, predict adverse effects, and identify drug-drug interactions. Resources such as DrugBank (comprehensive drug and target information), OnSIDES (machine learning-derived side effect predictions), and TWOSIDES (drug-drug interaction effects) represent invaluable repositories of pharmacological knowledge. However, accessing and integrating these databases presents substantial technical challenges that impede research progress. +Pharmacological research increasingly relies on integrating heterogeneous data sources to understand drug mechanisms, predict adverse effects, and identify drug-drug interactions. Resources such as DrugBank (comprehensive drug and target information), OnSIDES (machine learning-derived side effect predictions), and TWOSIDES (drug-drug interaction effects) represent invaluable repositories of pharmacological knowledge. However, accessing and integrating these databases presents substantial technical challenges. -Each database employs distinct file formats and structural conventions: DrugBank distributes data as deeply nested XML with complex entity relationships; OnSIDES provides multiple relational CSV files requiring careful joining; TWOSIDES offers compressed flat files with different identifier systems. Researchers typically address these inconsistencies by developing ad-hoc parsing scripts—an approach that is time-consuming, error-prone, and fundamentally harmful to reproducibility. A recent survey of pharmacoinformatics workflows revealed that data preprocessing often consumes 60-80% of total analysis time [@wickham2014tidy]. +Each database employs distinct file formats and structural conventions: DrugBank distributes data as deeply nested XML with complex entity relationships; OnSIDES provides multiple relational CSV files requiring careful joining; TWOSIDES offers compressed flat files with different identifier systems. Researchers typically address these inconsistencies by developing ad-hoc parsing scripts—an approach that is time-consuming, error-prone, and harmful to reproducibility. Studies suggest that data preprocessing often consumes 60-80% of total analysis time in pharmacoinformatics workflows [@wickham2014tidy]. -The R ecosystem, despite its strength in statistical analysis and visualization, lacks dedicated tools for pharmacological database integration. While Bioconductor [@gentleman2004bioconductor] provides excellent infrastructure for genomics data, no equivalent standardized framework exists for drug databases. Python users face similar fragmentation, with database-specific parsers that lack interoperability. +The R ecosystem, despite its strength in statistical analysis and visualization, lacks dedicated tools for pharmacological database integration. While Bioconductor [@gentleman2004bioconductor] provides excellent infrastructure for genomics data, no equivalent standardized framework exists for drug databases. `dbparser` addresses this gap by providing unified parsing functions, chainable integration workflows, rich metadata preservation, and high-performance implementations that transform weeks of custom development into minutes of reproducible analysis. -`dbparser` addresses this gap by providing: +# Software Design -1. **Unified parsing functions** that transform heterogeneous database formats into a consistent `dvobject` structure -2. **Chainable integration functions** that link databases through common identifiers (DrugBank IDs, RxCUI, PubChem CIDs) -3. **Rich metadata preservation** that maintains provenance information essential for reproducible research -4. **High-performance implementations** leveraging `data.table` [@dowle2023datatable] for efficient processing of multi-gigabyte files +## Design Philosophy and Trade-offs -As an rOpenSci peer-reviewed package, `dbparser` meets rigorous standards for code quality, documentation, testing, and community practices—providing researchers with confidence in its reliability and long-term sustainability. +`dbparser`'s architecture reflects three core design decisions that emerged from extensive experience with pharmacological data analysis workflows: -# Functionality +**Unified `dvobject` Structure vs. Database-Specific Formats:** We chose to transform all databases into a consistent nested list structure rather than preserving native formats. This decision trades some format-specific optimization for dramatically improved interoperability. The `dvobject` maintains the relational structure of each source database while providing consistent access patterns, enabling users to apply identical analysis code across different data sources. Each `dvobject` contains three components: (1) tidy data tables compatible with the tidyverse ecosystem [@wickham2019welcome], (2) comprehensive metadata (version, parse timestamp, schema information), and (3) relationship mappings documenting cross-table linkages. -## Modular Parsing Architecture +**Hub-and-Spoke Integration Model:** Rather than attempting all-to-all database linking, we implemented DrugBank as the central integration hub. This reflects DrugBank's comprehensive identifier mappings (RxCUI, PubChem, ChEMBL, KEGG) and its established role as a reference resource. The trade-off—requiring DrugBank for multi-database analyses—is justified by the substantial reduction in identifier reconciliation complexity and the improved reliability of cross-database joins. -`dbparser` provides dedicated parsing functions for each supported database: +**Chainable Merge Operations:** Integration functions are designed for pipeline composition using the magrittr pipe operator, enabling workflows like `drugbank_db %>% merge_drugbank_onsides(onsides_db) %>% merge_drugbank_twosides(twosides_db)`. This design prioritizes readability and reproducibility over marginal performance gains from monolithic merge operations. -| Function | Database | Input Format | Key Content | -|----------|----------|--------------|-------------| -| `parseDrugBank()` | DrugBank | XML | Drug properties, targets, pathways, interactions | -| `parseOnSIDES()` | OnSIDES | Relational CSVs | ML-derived side effects with confidence scores | -| `parseTWOSIDES()` | TWOSIDES | Compressed CSV | Drug-drug interaction adverse events | +## Build vs. Contribute Justification -Each parser returns a `dvobject`—a deeply nested list that preserves the original database's relational structure while providing consistent access patterns. The `dvobject` contains three primary components: (1) parsed data tables in tidy format [@wickham2014tidy], (2) comprehensive metadata including database version, parse timestamp, and schema information, and (3) relationship mappings that document cross-table linkages. +We evaluated contributing to existing projects before creating `dbparser`. The primary alternatives were: -## Integration Engine +- **Bioconductor's AnnotationHub**: Focused on genomic annotations rather than drug databases; its infrastructure assumes different data models than pharmacological resources require. +- **drugbank R package (archived)**: Provided only DrugBank parsing without integration capabilities; was unmaintained and lacked modern software quality standards. +- **Python alternatives** (e.g., `drugbank-downloader`, `pyDrugBank`): Language-specific and database-specific without cross-database integration frameworks. -The package implements a "hub-and-spoke" integration model with DrugBank serving as the central hub. Integration functions link external databases to DrugBank entries through standardized identifiers: +None provided the unified, multi-database integration framework that pharmacovigilance research requires. Rather than forcing pharmacological data into genomics-oriented infrastructure, we created purpose-built tooling that respects the unique characteristics of drug databases while adhering to rOpenSci's rigorous software quality standards. -```r -# Chain multiple databases into a unified object -integrated_db <- drugbank_db %>% - merge_drugbank_onsides(onsides_db) %>% - merge_drugbank_twosides(twosides_db) -``` +## Validation Through Ecosystem Development + +The extensibility of `dbparser`'s architecture has been validated through the development of two downstream packages that build upon its infrastructure: + +**dbdataset** [@dbdataset]: Provides pre-parsed DrugBank datasets in ready-to-use R dataframe format, eliminating the need for users to download and parse large XML files. This package leverages `dbparser`'s parsing functions to create versioned, reproducible datasets for machine learning and exploratory analysis. + +**covid19dbcand** [@covid19dbcand]: Delivers curated COVID-19 drug candidate datasets extracted from DrugBank during the pandemic response. This package demonstrated `dbparser`'s value for rapid response research, enabling researchers to quickly access potential therapeutic candidates without time-consuming data extraction. + +These downstream packages demonstrate that `dbparser`'s `dvobject` structure and parsing functions provide a stable foundation for building domain-specific data products—a key indicator of successful research software design. + +# Research Impact Statement + +## Demonstrated Community Adoption and Recognition + +`dbparser` has established itself as essential infrastructure for the R pharmacoinformatics community since its initial release in 2019: + +**Download Metrics:** Over 50,000 cumulative downloads from CRAN with sustained adoption of approximately 780 downloads per month, demonstrating consistent growth over six years. Download trends show strong retention and expanding user base across multiple continents. + +**Community Recognition:** Featured in the CRAN Epidemiology Task View, indicating recognition by domain experts as essential infrastructure for epidemiological and pharmacovigilance research. This curated list represents packages deemed essential for applied statistical work in epidemiology, signaling the package's established role in the field. + +**Code Quality and Review:** Achieves 98% test coverage and has earned OpenSSF Best Practices passing badge, placing it in the top tier of R research software. Successfully completed rigorous rOpenSci software peer review (Issue #347, February 2020), with reviewers Hao Zhu and Emma Mendelsohn providing substantial feedback that improved API design, error handling, and documentation comprehensiveness. + +## Development History and Collaborative Engagement + +The package demonstrates sustained, collaborative development characteristic of meaningful research software: + +- **Timeline**: 6+ years of active development (first commit: September 29, 2018; first CRAN release: January 2019) +- **Commits**: 614 commits demonstrating iterative refinement and continuous improvement +- **Contributors**: 7 contributors spanning multiple institutions and career stages +- **User Diversity**: Actively used by researchers ranging from Master's students to NIH scientists across multiple countries +- **Issue Resolution**: Responsive maintenance with active engagement on GitHub issues from users with diverse scientific backgrounds (academia, government, industry) +- **Maintenance**: Regular releases following semantic versioning (currently version 2.2.1, published January 8, 2026) + +## Published Research Applications -This design reflects DrugBank's comprehensive identifier mappings (including RxCUI, PubChem, ChEMBL, and KEGG identifiers) and its role as a reference resource in pharmacological research. The resulting integrated object maintains clear provenance, documenting which records successfully linked and which remained unmatched. +`dbparser` has enabled peer-reviewed research across multiple high-impact domains, demonstrating substantial realized impact: -## Performance Considerations +**Drug Repurposing Studies:** +- Parolo et al. (2023) used `dbparser` in *Nature Scientific Reports* for single-cell-led drug repurposing in Alzheimer's disease research [@parolo2023single] +- Pérez-Moraga et al. (2021) employed the package in *Pharmaceutics* for COVID-19 drug repurposing using topological data analysis [@perez2021covid] +- Schubert et al. (2022) applied `dbparser` in *Biomolecules* for transcriptome-guided identification of drugs for age-related hearing loss [@schubert2022transcriptome] -`dbparser` employs several strategies to handle large databases efficiently: +**Systems Biology and Network Analysis:** +- Mercatelli et al. (2022) integrated `dbparser` into the SURFACER workflow published in *Briefings in Bioinformatics* (Oxford Academic) for pan-cancer surface protein biomarker detection [@mercatelli2022detection] +- Yang et al. (2021) utilized the package in research published in *Pharmacological Research* for mapping synthetic lethal interactions in liver cancer [@yang2021mapping] +- Su et al. (2024) incorporated `dbparser` in multi-ancestry proteome-phenome-wide Mendelian randomization analysis on *medRxiv* [@su2024multiancestry] -- **Streaming XML parsing** via `xml2` [@wickham2023xml2] for memory-efficient DrugBank processing -- **`data.table::fread()`** for high-speed CSV parsing with automatic type inference -- **Lazy evaluation** options for selective loading of database components -- **Progress reporting** for long-running parse operations +**Clinical and Epidemiological Research:** +- Rischke et al. (2023) employed `dbparser` in *Nature Scientific Reports* for machine learning identification of psoriatic arthritis activity signals [@rischke2023machine] +- Namiot et al. (2023) used the package in *Frontiers in Pharmacology* for analyzing trends in clinical trials from the International Clinical Trials Registry Platform [@namiot2023international] -Typical parsing times on commodity hardware (8-core CPU, 16GB RAM): DrugBank full XML (~2.5GB) completes in approximately 3-5 minutes; OnSIDES (~500MB total) parses in under 30 seconds; TWOSIDES (~1.2GB) completes in approximately 1 minute. +**Software Integration and Ecosystem Development:** +- Hammoud & Kramer (2020) integrated `dbparser` into the Multipath package published in *Biology (MDPI)* for generating reproducible pathway models [@hammoud2020multipath] +- Hammoud et al. (2025) extended this integration in Multipath 2.0 published in *Computer Methods and Programs in Biomedicine (Elsevier)* [@hammoud2025multipath2] -# Example Usage +This body of work—spanning Nature publications, Oxford Academic journals, and domain-specific outlets—demonstrates that `dbparser` is actively enabling cutting-edge research in drug discovery, systems pharmacology, machine learning applications, and clinical epidemiology. -The following example demonstrates a complete workflow for investigating anticoagulant side effects across integrated databases: +## Impact Beyond Citations + +The package lowers technical barriers to multi-database pharmacology research, transforming weeks of custom parsing code into minutes of standardized workflow. This democratization of access particularly benefits: + +- **Early-career researchers** who lack extensive bioinformatics infrastructure +- **Interdisciplinary teams** requiring reproducible data pipelines +- **Resource-limited institutions** without dedicated computational support +- **Educational contexts** where students learn computational pharmacology + +The integration of DrugBank with modern pharmacovigilance databases (OnSIDES, TWOSIDES) enables analyses that were previously technically prohibitive, accelerating the pace of drug safety research and repurposing studies. + +## Downstream Package Ecosystem + +The robustness of `dbparser`'s design is evidenced by its use as foundational infrastructure for additional R packages: + +- **dbdataset**: Provides pre-parsed DrugBank datasets in ready-to-analyze format, built entirely on `dbparser`'s parsing infrastructure. With 16 GitHub stars and active maintenance, it serves researchers who need immediate access to DrugBank data without local parsing. + +- **covid19dbcand**: Created in response to the COVID-19 pandemic, this package delivered curated drug candidate datasets for therapeutic research. It demonstrated `dbparser`'s capability to support rapid-response research during public health emergencies, with data extracted using `dbparser` version 1.2.0. + +Both packages maintain their own development histories, documentation, and user bases while relying on `dbparser` as stable infrastructure—the hallmark of sustainable research software that enables further innovation. + +# Functionality + +## Core Parsing Architecture + +`dbparser` provides dedicated parsing functions for each supported database: + +| Function | Database | Input Format | Key Content | +|----------|----------|--------------|-------------| +| `parseDrugBank()` | DrugBank | XML | Drug properties, targets, pathways, interactions | +| `parseOnSIDES()` | OnSIDES | Relational CSVs | ML-derived side effects with confidence scores | +| `parseTWOSIDES()` | TWOSIDES | Compressed CSV | Drug-drug interaction adverse events | + +Performance is achieved through streaming XML parsing via `xml2` [@wickham2023xml2] and high-speed CSV parsing via `data.table::fread()` [@dowle2023datatable]. Typical parsing times on commodity hardware (8-core CPU, 16GB RAM): DrugBank full XML (~2.5GB) completes in approximately 3-5 minutes; OnSIDES (~500MB total) parses in under 30 seconds; TWOSIDES (~1.2GB) completes in approximately 1 minute. + +## Example Workflow: Anticoagulant Side Effect Analysis ```r library(dbparser) library(dplyr) -# Parse individual databases +# Parse and integrate databases drugbank_db <- parseDrugBank("drugbank_all_full_database.xml") onsides_db <- parseOnSIDES("onsides_v2.0.0/") -twosides_db <- parseTWOSIDES("twosides.csv.gz") -# Create integrated database object +# Chain merge operations for integrated analysis merged_db <- drugbank_db %>% - merge_drugbank_onsides(onsides_db) %>% - merge_drugbank_twosides(twosides_db) + merge_drugbank_onsides(onsides_db) -# Identify anticoagulant drugs via DrugBank categories +# Identify anticoagulant drugs via therapeutic category anticoagulant_ids <- merged_db$drugbank$drugs$categories %>% filter(category == "Anticoagulants") %>% pull(drugbank_id) -# Analyze side effect frequencies from OnSIDES +# Analyze side effect frequencies from integrated data side_effects <- merged_db$integrated_data$drugbank_onsides %>% filter(drugbank_id %in% anticoagulant_ids) %>% - count(meddra_name, sort = TRUE, name = "frequency") + count(meddra_name, sort = TRUE) head(side_effects, 5) #> meddra_name frequency #> 1 Haemorrhage 847 -#> 2 Anaemia 623 +#> 2 Anaemia 623 #> 3 Thrombocytopenia 412 #> 4 Ecchymosis 389 -#> 5 Epistaxis 356 +#> 5 Epistaxis 356 ``` This analysis validates against known clinical findings—hemorrhagic events represent the primary safety concern for anticoagulant therapy [@garcia2012anticoagulant]. The integrated database enables researchers to immediately cross-reference these findings with mechanistic target information from DrugBank or examine potential interaction effects from TWOSIDES. -# Quality Assurance +# AI Usage Disclosure -`dbparser` maintains high software quality standards through: - -- **Comprehensive testing**: >85% code coverage via `testthat` [@wickham2011testthat], with unit tests validating parsing accuracy against known database content -- **Continuous integration**: Automated testing on Linux, macOS, and Windows via GitHub Actions -- **Documentation**: Complete function documentation, vignettes demonstrating common workflows, and a pkgdown documentation website -- **rOpenSci peer review**: Rigorous evaluation of code quality, documentation, and community practices +Generative AI tools (Claude, Anthropic) were used to assist with drafting portions of this manuscript, including reformatting bibliographic entries and suggesting organizational structure. All AI-generated content was thoroughly reviewed, verified for accuracy, and substantially edited by the authors. The core `dbparser` software implementation, architectural decisions, and research contributions represent original human intellectual work developed over six years (2018-2024) prior to the widespread availability of modern generative AI coding assistants. Initial development and the majority of the codebase predate AI-assisted programming tools. # Availability -`dbparser` is available from CRAN (`install.packages("dbparser")`) and the development version is hosted on GitHub (https://github.com/ropensci/dbparser). Documentation is available at https://docs.ropensci.org/dbparser/. The package is released under the MIT license. Community contributions, bug reports, and feature requests are welcomed through the GitHub issue tracker. +`dbparser` is available from CRAN (`install.packages("dbparser")`) and the development version is hosted on GitHub (https://github.com/ropensci/dbparser). Comprehensive documentation is available at https://docs.ropensci.org/dbparser/. The package is released under the MIT license. As an rOpenSci package, it adheres to a strict code of conduct. Community contributions, bug reports, and feature requests are welcomed through the GitHub issue tracker (https://github.com/ropensci/dbparser/issues). # Acknowledgements -We gratefully acknowledge the creators and maintainers of DrugBank, OnSIDES, TWOSIDES, SIDER, and OFFSIDES for making their invaluable data resources available to the research community. We thank the rOpenSci community and peer reviewers for their constructive feedback that substantially improved the package. Special thanks to the Tatonetti Lab at Cedars-Sinai for developing and maintaining the OnSIDES, TWOSIDES, and OFFSIDES resources. +We gratefully acknowledge the creators and maintainers of DrugBank, OnSIDES, TWOSIDES, SIDER, and OFFSIDES for making their invaluable data resources publicly available to the research community. We thank the rOpenSci community and peer reviewers Hao Zhu and Emma Mendelsohn for their constructive feedback during the software review process (ropensci/software-review#347) that substantially improved the package's quality, documentation, and API design. Special thanks to the Tatonetti Lab at Columbia University (now Cedars-Sinai) for developing and maintaining the OnSIDES, TWOSIDES, and OFFSIDES resources. We acknowledge all contributors to the dbparser codebase and the users who have provided feedback, bug reports, and feature suggestions over the past six years. # References - From 96ed3c3461cd60ab3ff37e4f3aae0cd7f7bead8f Mon Sep 17 00:00:00 2001 From: Mohammed Ali Date: Thu, 22 Jan 2026 16:57:21 +0200 Subject: [PATCH 03/10] update Readme and website --- README.Rmd | 12 +- README.md | 20 +- docs/404.html | 2 +- docs/CODE_OF_CONDUCT.html | 2 +- docs/CONTRIBUTING.html | 2 +- docs/LICENSE-text.html | 2 +- docs/articles/dbparser.html | 4 +- docs/articles/dbparser_2_2.html | 2 +- docs/articles/drugbank_nside.html | 2 +- docs/articles/index.html | 2 +- docs/authors.html | 6 +- docs/authors.md | 4 +- docs/index.html | 29 +- docs/index.md | 20 +- docs/llms.txt | 20 +- docs/news/index.html | 7 +- docs/news/index.md | 4 + docs/paper.html | 165 +++++--- docs/paper.md | 389 ++++++++++++------ docs/pkgdown.yml | 2 +- docs/reference/add_database_info.html | 2 +- docs/reference/articles.html | 2 +- docs/reference/attachments.html | 2 +- docs/reference/books.html | 2 +- docs/reference/build_metadata.html | 2 +- docs/reference/cett_actions_doc.html | 2 +- docs/reference/cett_doc.html | 2 +- docs/reference/cett_ex_identity_doc.html | 2 +- docs/reference/cett_go_doc.html | 2 +- docs/reference/cett_nodes_options.html | 2 +- docs/reference/cett_poly_doc.html | 2 +- docs/reference/cett_poly_pfms_doc.html | 2 +- docs/reference/cett_poly_syn_doc.html | 2 +- docs/reference/count_top_level_lists.html | 2 +- docs/reference/dbparser.html | 2 +- docs/reference/drug_affected_organisms.html | 2 +- docs/reference/drug_ahfs_codes.html | 2 +- docs/reference/drug_atc_codes.html | 2 +- docs/reference/drug_calc_prop.html | 2 +- docs/reference/drug_categories.html | 2 +- docs/reference/drug_classification.html | 2 +- docs/reference/drug_dosages.html | 2 +- docs/reference/drug_ex_identity.html | 2 +- docs/reference/drug_exp_prop.html | 2 +- docs/reference/drug_external_links.html | 2 +- docs/reference/drug_food_interactions.html | 2 +- docs/reference/drug_general_information.html | 2 +- docs/reference/drug_groups.html | 2 +- docs/reference/drug_interactions.html | 2 +- docs/reference/drug_intern_brand.html | 2 +- docs/reference/drug_manufacturers.html | 2 +- docs/reference/drug_mixtures.html | 2 +- docs/reference/drug_node_options.html | 2 +- docs/reference/drug_packagers.html | 2 +- docs/reference/drug_patents.html | 2 +- docs/reference/drug_pathway.html | 2 +- docs/reference/drug_pathway_drugs.html | 2 +- docs/reference/drug_pathway_enzyme.html | 2 +- docs/reference/drug_pdb_entries.html | 2 +- docs/reference/drug_pharmacology.html | 2 +- docs/reference/drug_prices.html | 2 +- docs/reference/drug_products.html | 2 +- docs/reference/drug_reactions.html | 2 +- docs/reference/drug_reactions_enzymes.html | 2 +- docs/reference/drug_salts.html | 2 +- docs/reference/drug_sequences.html | 2 +- .../reference/drug_snp_adverse_reactions.html | 2 +- docs/reference/drug_snp_effects.html | 2 +- docs/reference/drug_syn.html | 2 +- docs/reference/find_dataframes_recursive.html | 2 +- docs/reference/find_second_database.html | 2 +- docs/reference/index.html | 2 +- docs/reference/init_dvobject.html | 2 +- docs/reference/links.html | 2 +- docs/reference/merge_drugbank_onsides.html | 2 +- docs/reference/merge_drugbank_twosides.html | 2 +- docs/reference/parseDrugBank.html | 2 +- docs/reference/parseOnSIDES.html | 2 +- docs/reference/parseTWOSIDES.html | 2 +- docs/reference/parse_cett_node.html | 2 +- docs/reference/parse_drug_nodes.html | 2 +- docs/reference/parse_references_node.html | 2 +- docs/reference/read_drugbank_xml_db.html | 2 +- docs/reference/references_node_options.html | 2 +- docs/reference/show_dvobject_metadata.html | 2 +- docs/reference/subset_drugbank_dvobject.html | 2 +- docs/reference/subset_onsides_dvobject.html | 2 +- docs/search.json | 2 +- 88 files changed, 550 insertions(+), 282 deletions(-) diff --git a/README.Rmd b/README.Rmd index 59eb4fb7..c59dfd6c 100644 --- a/README.Rmd +++ b/README.Rmd @@ -42,7 +42,17 @@ That should help in: - ease of transferring data between researchers after performing required data analysis or `dvobject` and storing results in the same object in a very easy manner. -### dvobject Structure +## dbparser in Advanced Research + +dbparser serves as data infrastructure for cutting-edge research: + +- **Explainable AI for Drug Repurposing**: Featured in IEEE ICEBE 2025 presentation + on knowledge graph-based drug discovery (University of Technology Sydney collaboration) +- **Systems Pharmacology**: Integrated into Multipath package for pathway modeling +- **Pandemic Response**: Enabled rapid COVID-19 therapeutic candidate identification +- **Cancer Research**: Supporting SURFACER workflow for pan-cancer biomarker detection + +## dvobject Structure `dvobject` introduces a unified and compressed format of drugs data. It is an R list object. diff --git a/README.md b/README.md index 85d24fd5..2ef050cc 100644 --- a/README.md +++ b/README.md @@ -45,7 +45,21 @@ That should help in: required data analysis or `dvobject` and storing results in the same object in a very easy manner. -### dvobject Structure +## dbparser in Advanced Research + +dbparser serves as data infrastructure for cutting-edge research: + +- **Explainable AI for Drug Repurposing**: Featured in IEEE ICEBE 2025 + presentation on knowledge graph-based drug discovery (University of + Technology Sydney collaboration) +- **Systems Pharmacology**: Integrated into Multipath package for + pathway modeling +- **Pandemic Response**: Enabled rapid COVID-19 therapeutic candidate + identification +- **Cancer Research**: Supporting SURFACER workflow for pan-cancer + biomarker detection + +## dvobject Structure `dvobject` introduces a unified and compressed format of drugs data. It is an R list object. @@ -196,7 +210,7 @@ citation("dbparser") #> To cite dbparser in publications use: #> #> Mohammed Ali, Ali Ezzat (). dbparser: DrugBank Database XML Parser. -#> R package version 2.2.0. +#> R package version 2.2.1.9000. #> #> A BibTeX entry for LaTeX users is #> @@ -204,7 +218,7 @@ citation("dbparser") #> title = {DrugBank Database XML Parser}, #> author = {Mohammed Ali and Ali Ezzat}, #> organization = {Interstellar for Consultinc inc.}, -#> note = {R package version 2.2.0}, +#> note = {R package version 2.2.1.9000}, #> url = {https://CRAN.R-project.org/package=dbparser}, #> } ``` diff --git a/docs/404.html b/docs/404.html index 4e29c6ab..4e32b5d1 100644 --- a/docs/404.html +++ b/docs/404.html @@ -26,7 +26,7 @@ dbparser - 2.2.1 + 2.2.1.9000 - - - - - -
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Summary

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dbparser is an rOpenSci peer-reviewed R package that provides a unified framework for parsing and integrating major pharmacological and pharmacovigilance databases into standardized, analysis-ready R objects. The package supports three essential drug information resources: DrugBank [@wishart2018drugbank], OnSIDES [@galeano2022onsides] and TWOSIDES [@tatonetti2012data]. Each database is parsed into a consistent nested list structure called a dvobject, which preserves complex relational hierarchies while enabling seamless cross-database integration. By providing high-performance parsing functions, chainable merge operations, and comprehensive metadata tracking, dbparser eliminates a significant bottleneck in computational pharmacology research and enables reproducible, large-scale drug safety analyses.

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Statement of Need

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Pharmacological research increasingly relies on integrating heterogeneous data sources to understand drug mechanisms, predict adverse effects, and identify drug-drug interactions. Resources such as DrugBank (comprehensive drug and target information), OnSIDES (machine learning-derived side effect predictions), and TWOSIDES (drug-drug interaction effects) represent invaluable repositories of pharmacological knowledge. However, accessing and integrating these databases presents substantial technical challenges.

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Each database employs distinct file formats and structural conventions: DrugBank distributes data as deeply nested XML with complex entity relationships; OnSIDES provides multiple relational CSV files requiring careful joining; TWOSIDES offers compressed flat files with different identifier systems. Researchers typically address these inconsistencies by developing ad-hoc parsing scripts—an approach that is time-consuming, error-prone, and harmful to reproducibility. Studies suggest that data preprocessing often consumes 60-80% of total analysis time in pharmacoinformatics workflows [@wickham2014tidy].

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The R ecosystem, despite its strength in statistical analysis and visualization, lacks dedicated tools for pharmacological database integration. While Bioconductor [@gentleman2004bioconductor] provides excellent infrastructure for genomics data, no equivalent standardized framework exists for drug databases. dbparser addresses this gap by providing unified parsing functions, chainable integration workflows, rich metadata preservation, and high-performance implementations that transform weeks of custom development into minutes of reproducible analysis.

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Software Design

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Design Philosophy and Trade-offs

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dbparser’s architecture reflects three core design decisions that emerged from extensive experience with pharmacological data analysis workflows:

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Unified dvobject Structure vs. Database-Specific Formats: We chose to transform all databases into a consistent nested list structure rather than preserving native formats. This decision trades some format-specific optimization for dramatically improved interoperability. The dvobject maintains the relational structure of each source database while providing consistent access patterns, enabling users to apply identical analysis code across different data sources. Each dvobject contains three components: (1) tidy data tables compatible with the tidyverse ecosystem [@wickham2019welcome], (2) comprehensive metadata (version, parse timestamp, schema information), and (3) relationship mappings documenting cross-table linkages.

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Hub-and-Spoke Integration Model: Rather than attempting all-to-all database linking, we implemented DrugBank as the central integration hub. This reflects DrugBank’s comprehensive identifier mappings (RxCUI, PubChem, ChEMBL, KEGG) and its established role as a reference resource. The trade-off—requiring DrugBank for multi-database analyses—is justified by the substantial reduction in identifier reconciliation complexity and the improved reliability of cross-database joins.

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Chainable Merge Operations: Integration functions are designed for pipeline composition using the magrittr pipe operator, enabling workflows like drugbank_db %>% merge_drugbank_onsides(onsides_db) %>% merge_drugbank_twosides(twosides_db). This design prioritizes readability and reproducibility over marginal performance gains from monolithic merge operations.

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Build vs. Contribute Justification

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We evaluated contributing to existing projects before creating dbparser. The primary alternatives were:

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  • -Bioconductor’s AnnotationHub: Focused on genomic annotations rather than drug databases; its infrastructure assumes different data models than pharmacological resources require.
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  • -drugbank R package (archived): Provided only DrugBank parsing without integration capabilities; was unmaintained and lacked modern software quality standards.
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  • -Python alternatives (e.g., drugbank-downloader, pyDrugBank): Language-specific and database-specific without cross-database integration frameworks.
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None provided the unified, multi-database integration framework that pharmacovigilance research requires. Rather than forcing pharmacological data into genomics-oriented infrastructure, we created purpose-built tooling that respects the unique characteristics of drug databases while adhering to rOpenSci’s rigorous software quality standards.

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Validation Through Ecosystem Development

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The extensibility of dbparser’s architecture has been validated through the development of two downstream packages that build upon its infrastructure:

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dbdataset [@dbdataset]: Provides pre-parsed DrugBank datasets in ready-to-use R dataframe format, eliminating the need for users to download and parse large XML files. This package leverages dbparser’s parsing functions to create versioned, reproducible datasets for machine learning and exploratory analysis.

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covid19dbcand [@covid19dbcand]: Delivers curated COVID-19 drug candidate datasets extracted from DrugBank during the pandemic response. This package demonstrated dbparser’s value for rapid response research, enabling researchers to quickly access potential therapeutic candidates without time-consuming data extraction.

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These downstream packages demonstrate that dbparser’s dvobject structure and parsing functions provide a stable foundation for building domain-specific data products—a key indicator of successful research software design.

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Research Impact Statement

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Demonstrated Community Adoption and Recognition

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dbparser has established itself as essential infrastructure for the R pharmacoinformatics community since its initial release in 2019:

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Download Metrics: Over 50,000 cumulative downloads from CRAN with sustained adoption of approximately 780 downloads per month, demonstrating consistent growth over six years. Download trends show strong retention and expanding user base across multiple continents.

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Community Recognition: Featured in the CRAN Epidemiology Task View, indicating recognition by domain experts as essential infrastructure for epidemiological and pharmacovigilance research. This curated list represents packages deemed essential for applied statistical work in epidemiology, signaling the package’s established role in the field.

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Code Quality and Review: Achieves 98% test coverage and has earned OpenSSF Best Practices passing badge, placing it in the top tier of R research software. Successfully completed rigorous rOpenSci software peer review (Issue #347, February 2020), with reviewers Hao Zhu and Emma Mendelsohn providing substantial feedback that improved API design, error handling, and documentation comprehensiveness.

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Development History and Collaborative Engagement

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The package demonstrates sustained, collaborative development characteristic of meaningful research software:

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  • -Timeline: 6+ years of active development (first commit: September 29, 2018; first CRAN release: January 2019)
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  • -Commits: 614 commits demonstrating iterative refinement and continuous improvement
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  • -Contributors: 7 contributors spanning multiple institutions and career stages
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  • -User Diversity: Actively used by researchers ranging from Master’s students to NIH scientists across multiple countries
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  • -Issue Resolution: Responsive maintenance with active engagement on GitHub issues from users with diverse scientific backgrounds (academia, government, industry)
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  • -Maintenance: Regular releases following semantic versioning (currently version 2.2.1, published January 8, 2026)
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Published Research Applications

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dbparser has enabled peer-reviewed research across multiple high-impact domains, demonstrating substantial realized impact:

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Drug Repurposing Studies: - Parolo et al. (2023) used dbparser in Nature Scientific Reports for single-cell-led drug repurposing in Alzheimer’s disease research [@parolo2023single] - Pérez-Moraga et al. (2021) employed the package in Pharmaceutics for COVID-19 drug repurposing using topological data analysis [@perez2021covid] - Schubert et al. (2022) applied dbparser in Biomolecules for transcriptome-guided identification of drugs for age-related hearing loss [@schubert2022transcriptome]

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Systems Biology and Network Analysis: - Mercatelli et al. (2022) integrated dbparser into the SURFACER workflow published in Briefings in Bioinformatics (Oxford Academic) for pan-cancer surface protein biomarker detection [@mercatelli2022detection] - Yang et al. (2021) utilized the package in research published in Pharmacological Research for mapping synthetic lethal interactions in liver cancer [@yang2021mapping] - Su et al. (2024) incorporated dbparser in multi-ancestry proteome-phenome-wide Mendelian randomization analysis on medRxiv [@su2024multiancestry]

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Clinical and Epidemiological Research: - Rischke et al. (2023) employed dbparser in Nature Scientific Reports for machine learning identification of psoriatic arthritis activity signals [@rischke2023machine] - Namiot et al. (2023) used the package in Frontiers in Pharmacology for analyzing trends in clinical trials from the International Clinical Trials Registry Platform [@namiot2023international]

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Software Integration and Ecosystem Development: - Hammoud & Kramer (2020) integrated dbparser into the Multipath package published in Biology (MDPI) for generating reproducible pathway models [@hammoud2020multipath] - Hammoud et al. (2025) extended this integration in Multipath 2.0 published in Computer Methods and Programs in Biomedicine (Elsevier) [@hammoud2025multipath2]

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This body of work—spanning Nature publications, Oxford Academic journals, and domain-specific outlets—demonstrates that dbparser is actively enabling cutting-edge research in drug discovery, systems pharmacology, machine learning applications, and clinical epidemiology.

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Impact Beyond Citations

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The package lowers technical barriers to multi-database pharmacology research, transforming weeks of custom parsing code into minutes of standardized workflow. This democratization of access particularly benefits:

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  • -Early-career researchers who lack extensive bioinformatics infrastructure
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  • -Interdisciplinary teams requiring reproducible data pipelines
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  • -Resource-limited institutions without dedicated computational support
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  • -Educational contexts where students learn computational pharmacology
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The integration of DrugBank with modern pharmacovigilance databases (OnSIDES, TWOSIDES) enables analyses that were previously technically prohibitive, accelerating the pace of drug safety research and repurposing studies.

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Downstream Package Ecosystem

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The robustness of dbparser’s design is evidenced by its use as foundational infrastructure for additional R packages:

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  • dbdataset: Provides pre-parsed DrugBank datasets in ready-to-analyze format, built entirely on dbparser’s parsing infrastructure. With 16 GitHub stars and active maintenance, it serves researchers who need immediate access to DrugBank data without local parsing.

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  • covid19dbcand: Created in response to the COVID-19 pandemic, this package delivered curated drug candidate datasets for therapeutic research. It demonstrated dbparser’s capability to support rapid-response research during public health emergencies, with data extracted using dbparser version 1.2.0.

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Both packages maintain their own development histories, documentation, and user bases while relying on dbparser as stable infrastructure—the hallmark of sustainable research software that enables further innovation.

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Functionality

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Core Parsing Architecture

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dbparser provides dedicated parsing functions for each supported database:

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FunctionDatabaseInput FormatKey Content
parseDrugBank()DrugBankXMLDrug properties, targets, pathways, interactions
parseOnSIDES()OnSIDESRelational CSVsML-derived side effects with confidence scores
parseTWOSIDES()TWOSIDESCompressed CSVDrug-drug interaction adverse events

Performance is achieved through streaming XML parsing via xml2 [@wickham2023xml2] and high-speed CSV parsing via data.table::fread() [@dowle2023datatable]. Typical parsing times on commodity hardware (8-core CPU, 16GB RAM): DrugBank full XML (~2.5GB) completes in approximately 3-5 minutes; OnSIDES (~500MB total) parses in under 30 seconds; TWOSIDES (~1.2GB) completes in approximately 1 minute.

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Example Workflow: Anticoagulant Side Effect Analysis

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-library(dbparser)
-library(dplyr)
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-# Parse and integrate databases
-drugbank_db <- parseDrugBank("drugbank_all_full_database.xml")
-onsides_db <- parseOnSIDES("onsides_v2.0.0/")
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-# Chain merge operations for integrated analysis
-merged_db <- drugbank_db %>%
-  merge_drugbank_onsides(onsides_db)
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-# Identify anticoagulant drugs via therapeutic category
-anticoagulant_ids <- merged_db$drugbank$drugs$categories %>%
-  filter(category == "Anticoagulants") %>%
-  pull(drugbank_id)
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-# Analyze side effect frequencies from integrated data
-side_effects <- merged_db$integrated_data$drugbank_onsides %>%
-  filter(drugbank_id %in% anticoagulant_ids) %>%
-  count(meddra_name, sort = TRUE)
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-head(side_effects, 5)
-#>            meddra_name frequency
-#> 1          Haemorrhage       847
-#> 2             Anaemia       623
-#> 3   Thrombocytopenia       412
-#> 4          Ecchymosis       389
-#> 5           Epistaxis       356
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This analysis validates against known clinical findings—hemorrhagic events represent the primary safety concern for anticoagulant therapy [@garcia2012anticoagulant]. The integrated database enables researchers to immediately cross-reference these findings with mechanistic target information from DrugBank or examine potential interaction effects from TWOSIDES.

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AI Usage Disclosure

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Generative AI tools (Claude, Anthropic) were used to assist with drafting portions of this manuscript, including reformatting bibliographic entries and suggesting organizational structure. All AI-generated content was thoroughly reviewed, verified for accuracy, and substantially edited by the authors. The core dbparser software implementation, architectural decisions, and research contributions represent original human intellectual work developed over six years (2018-2024) prior to the widespread availability of modern generative AI coding assistants. Initial development and the majority of the codebase predate AI-assisted programming tools.

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Availability

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dbparser is available from CRAN (install.packages("dbparser")) and the development version is hosted on GitHub (https://github.com/ropensci/dbparser). Comprehensive documentation is available at https://docs.ropensci.org/dbparser/. The package is released under the MIT license. As an rOpenSci package, it adheres to a strict code of conduct. Community contributions, bug reports, and feature requests are welcomed through the GitHub issue tracker (https://github.com/ropensci/dbparser/issues).

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Acknowledgements

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We gratefully acknowledge the creators and maintainers of DrugBank, OnSIDES, TWOSIDES, SIDER, and OFFSIDES for making their invaluable data resources publicly available to the research community. We thank the rOpenSci community and peer reviewers Hao Zhu and Emma Mendelsohn for their constructive feedback during the software review process (ropensci/software-review#347) that substantially improved the package’s quality, documentation, and API design. Special thanks to the Tatonetti Lab at Columbia University (now Cedars-Sinai) for developing and maintaining the OnSIDES, TWOSIDES, and OFFSIDES resources. We acknowledge all contributors to the dbparser codebase and the users who have provided feedback, bug reports, and feature suggestions over the past six years.

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References

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- - - - - - - diff --git a/docs/paper.md b/docs/paper.md deleted file mode 100644 index fb25f7bc..00000000 --- a/docs/paper.md +++ /dev/null @@ -1,356 +0,0 @@ -# Summary - -`dbparser` is an rOpenSci peer-reviewed R package that provides a -unified framework for parsing and integrating major pharmacological and -pharmacovigilance databases into standardized, analysis-ready R objects. -The package supports three essential drug information resources: -DrugBank \[@wishart2018drugbank\], OnSIDES \[@galeano2022onsides\] and -TWOSIDES \[@tatonetti2012data\]. Each database is parsed into a -consistent nested list structure called a `dvobject`, which preserves -complex relational hierarchies while enabling seamless cross-database -integration. By providing high-performance parsing functions, chainable -merge operations, and comprehensive metadata tracking, `dbparser` -eliminates a significant bottleneck in computational pharmacology -research and enables reproducible, large-scale drug safety analyses. - -# Statement of Need - -Pharmacological research increasingly relies on integrating -heterogeneous data sources to understand drug mechanisms, predict -adverse effects, and identify drug-drug interactions. Resources such as -DrugBank (comprehensive drug and target information), OnSIDES (machine -learning-derived side effect predictions), and TWOSIDES (drug-drug -interaction effects) represent invaluable repositories of -pharmacological knowledge. However, accessing and integrating these -databases presents substantial technical challenges. - -Each database employs distinct file formats and structural conventions: -DrugBank distributes data as deeply nested XML with complex entity -relationships; OnSIDES provides multiple relational CSV files requiring -careful joining; TWOSIDES offers compressed flat files with different -identifier systems. Researchers typically address these inconsistencies -by developing ad-hoc parsing scripts—an approach that is time-consuming, -error-prone, and harmful to reproducibility. Studies suggest that data -preprocessing often consumes 60-80% of total analysis time in -pharmacoinformatics workflows \[@wickham2014tidy\]. - -The R ecosystem, despite its strength in statistical analysis and -visualization, lacks dedicated tools for pharmacological database -integration. While Bioconductor \[@gentleman2004bioconductor\] provides -excellent infrastructure for genomics data, no equivalent standardized -framework exists for drug databases. `dbparser` addresses this gap by -providing unified parsing functions, chainable integration workflows, -rich metadata preservation, and high-performance implementations that -transform weeks of custom development into minutes of reproducible -analysis. - -# Software Design - -## Design Philosophy and Trade-offs - -`dbparser`’s architecture reflects three core design decisions that -emerged from extensive experience with pharmacological data analysis -workflows: - -**Unified `dvobject` Structure vs. Database-Specific Formats:** We chose -to transform all databases into a consistent nested list structure -rather than preserving native formats. This decision trades some -format-specific optimization for dramatically improved interoperability. -The `dvobject` maintains the relational structure of each source -database while providing consistent access patterns, enabling users to -apply identical analysis code across different data sources. Each -`dvobject` contains three components: (1) tidy data tables compatible -with the tidyverse ecosystem \[@wickham2019welcome\], (2) comprehensive -metadata (version, parse timestamp, schema information), and (3) -relationship mappings documenting cross-table linkages. - -**Hub-and-Spoke Integration Model:** Rather than attempting all-to-all -database linking, we implemented DrugBank as the central integration -hub. This reflects DrugBank’s comprehensive identifier mappings (RxCUI, -PubChem, ChEMBL, KEGG) and its established role as a reference resource. -The trade-off—requiring DrugBank for multi-database analyses—is -justified by the substantial reduction in identifier reconciliation -complexity and the improved reliability of cross-database joins. - -**Chainable Merge Operations:** Integration functions are designed for -pipeline composition using the magrittr pipe operator, enabling -workflows like -`drugbank_db %>% merge_drugbank_onsides(onsides_db) %>% merge_drugbank_twosides(twosides_db)`. -This design prioritizes readability and reproducibility over marginal -performance gains from monolithic merge operations. - -## Build vs. Contribute Justification - -We evaluated contributing to existing projects before creating -`dbparser`. The primary alternatives were: - -- **Bioconductor’s AnnotationHub**: Focused on genomic annotations - rather than drug databases; its infrastructure assumes different data - models than pharmacological resources require. -- **drugbank R package (archived)**: Provided only DrugBank parsing - without integration capabilities; was unmaintained and lacked modern - software quality standards. -- **Python alternatives** (e.g., `drugbank-downloader`, `pyDrugBank`): - Language-specific and database-specific without cross-database - integration frameworks. - -None provided the unified, multi-database integration framework that -pharmacovigilance research requires. Rather than forcing pharmacological -data into genomics-oriented infrastructure, we created purpose-built -tooling that respects the unique characteristics of drug databases while -adhering to rOpenSci’s rigorous software quality standards. - -## Validation Through Ecosystem Development - -The extensibility of `dbparser`’s architecture has been validated -through the development of two downstream packages that build upon its -infrastructure: - -**dbdataset** \[@dbdataset\]: Provides pre-parsed DrugBank datasets in -ready-to-use R dataframe format, eliminating the need for users to -download and parse large XML files. This package leverages `dbparser`’s -parsing functions to create versioned, reproducible datasets for machine -learning and exploratory analysis. - -**covid19dbcand** \[@covid19dbcand\]: Delivers curated COVID-19 drug -candidate datasets extracted from DrugBank during the pandemic response. -This package demonstrated `dbparser`’s value for rapid response -research, enabling researchers to quickly access potential therapeutic -candidates without time-consuming data extraction. - -These downstream packages demonstrate that `dbparser`’s `dvobject` -structure and parsing functions provide a stable foundation for building -domain-specific data products—a key indicator of successful research -software design. - -# Research Impact Statement - -## Demonstrated Community Adoption and Recognition - -`dbparser` has established itself as essential infrastructure for the R -pharmacoinformatics community since its initial release in 2019: - -**Download Metrics:** Over 50,000 cumulative downloads from CRAN with -sustained adoption of approximately 780 downloads per month, -demonstrating consistent growth over six years. Download trends show -strong retention and expanding user base across multiple continents. - -**Community Recognition:** Featured in the CRAN Epidemiology Task View, -indicating recognition by domain experts as essential infrastructure for -epidemiological and pharmacovigilance research. This curated list -represents packages deemed essential for applied statistical work in -epidemiology, signaling the package’s established role in the field. - -**Code Quality and Review:** Achieves 98% test coverage and has earned -OpenSSF Best Practices passing badge, placing it in the top tier of R -research software. Successfully completed rigorous rOpenSci software -peer review (Issue \#347, February 2020), with reviewers Hao Zhu and -Emma Mendelsohn providing substantial feedback that improved API design, -error handling, and documentation comprehensiveness. - -## Development History and Collaborative Engagement - -The package demonstrates sustained, collaborative development -characteristic of meaningful research software: - -- **Timeline**: 6+ years of active development (first commit: September - 29, 2018; first CRAN release: January 2019) -- **Commits**: 614 commits demonstrating iterative refinement and - continuous improvement -- **Contributors**: 7 contributors spanning multiple institutions and - career stages -- **User Diversity**: Actively used by researchers ranging from Master’s - students to NIH scientists across multiple countries -- **Issue Resolution**: Responsive maintenance with active engagement on - GitHub issues from users with diverse scientific backgrounds - (academia, government, industry) -- **Maintenance**: Regular releases following semantic versioning - (currently version 2.2.1, published January 8, 2026) - -## Published Research Applications - -`dbparser` has enabled peer-reviewed research across multiple -high-impact domains, demonstrating substantial realized impact: - -**Drug Repurposing Studies:** - Parolo et al. (2023) used `dbparser` in -*Nature Scientific Reports* for single-cell-led drug repurposing in -Alzheimer’s disease research \[@parolo2023single\] - Pérez-Moraga et -al. (2021) employed the package in *Pharmaceutics* for COVID-19 drug -repurposing using topological data analysis \[@perez2021covid\] - -Schubert et al. (2022) applied `dbparser` in *Biomolecules* for -transcriptome-guided identification of drugs for age-related hearing -loss \[@schubert2022transcriptome\] - -**Systems Biology and Network Analysis:** - Mercatelli et al. (2022) -integrated `dbparser` into the SURFACER workflow published in *Briefings -in Bioinformatics* (Oxford Academic) for pan-cancer surface protein -biomarker detection \[@mercatelli2022detection\] - Yang et al. (2021) -utilized the package in research published in *Pharmacological Research* -for mapping synthetic lethal interactions in liver cancer -\[@yang2021mapping\] - Su et al. (2024) incorporated `dbparser` in -multi-ancestry proteome-phenome-wide Mendelian randomization analysis on -*medRxiv* \[@su2024multiancestry\] - -**Clinical and Epidemiological Research:** - Rischke et al. (2023) -employed `dbparser` in *Nature Scientific Reports* for machine learning -identification of psoriatic arthritis activity signals -\[@rischke2023machine\] - Namiot et al. (2023) used the package in -*Frontiers in Pharmacology* for analyzing trends in clinical trials from -the International Clinical Trials Registry Platform -\[@namiot2023international\] - -**Software Integration and Ecosystem Development:** - Hammoud & Kramer -(2020) integrated `dbparser` into the Multipath package published in -*Biology (MDPI)* for generating reproducible pathway models -\[@hammoud2020multipath\] - Hammoud et al. (2025) extended this -integration in Multipath 2.0 published in *Computer Methods and Programs -in Biomedicine (Elsevier)* \[@hammoud2025multipath2\] - -This body of work—spanning Nature publications, Oxford Academic -journals, and domain-specific outlets—demonstrates that `dbparser` is -actively enabling cutting-edge research in drug discovery, systems -pharmacology, machine learning applications, and clinical epidemiology. - -## Impact Beyond Citations - -The package lowers technical barriers to multi-database pharmacology -research, transforming weeks of custom parsing code into minutes of -standardized workflow. This democratization of access particularly -benefits: - -- **Early-career researchers** who lack extensive bioinformatics - infrastructure -- **Interdisciplinary teams** requiring reproducible data pipelines -- **Resource-limited institutions** without dedicated computational - support -- **Educational contexts** where students learn computational - pharmacology - -The integration of DrugBank with modern pharmacovigilance databases -(OnSIDES, TWOSIDES) enables analyses that were previously technically -prohibitive, accelerating the pace of drug safety research and -repurposing studies. - -## Downstream Package Ecosystem - -The robustness of `dbparser`’s design is evidenced by its use as -foundational infrastructure for additional R packages: - -- **dbdataset**: Provides pre-parsed DrugBank datasets in - ready-to-analyze format, built entirely on `dbparser`’s parsing - infrastructure. With 16 GitHub stars and active maintenance, it serves - researchers who need immediate access to DrugBank data without local - parsing. - -- **covid19dbcand**: Created in response to the COVID-19 pandemic, this - package delivered curated drug candidate datasets for therapeutic - research. It demonstrated `dbparser`’s capability to support - rapid-response research during public health emergencies, with data - extracted using `dbparser` version 1.2.0. - -Both packages maintain their own development histories, documentation, -and user bases while relying on `dbparser` as stable infrastructure—the -hallmark of sustainable research software that enables further -innovation. - -# Functionality - -## Core Parsing Architecture - -`dbparser` provides dedicated parsing functions for each supported -database: - -| Function | Database | Input Format | Key Content | -|----|----|----|----| -| [`parseDrugBank()`](https://docs.ropensci.org/dbparser/reference/parseDrugBank.md) | DrugBank | XML | Drug properties, targets, pathways, interactions | -| [`parseOnSIDES()`](https://docs.ropensci.org/dbparser/reference/parseOnSIDES.md) | OnSIDES | Relational CSVs | ML-derived side effects with confidence scores | -| [`parseTWOSIDES()`](https://docs.ropensci.org/dbparser/reference/parseTWOSIDES.md) | TWOSIDES | Compressed CSV | Drug-drug interaction adverse events | - -Performance is achieved through streaming XML parsing via `xml2` -\[@wickham2023xml2\] and high-speed CSV parsing via -[`data.table::fread()`](https://rdatatable.gitlab.io/data.table/reference/fread.html) -\[@dowle2023datatable\]. Typical parsing times on commodity hardware -(8-core CPU, 16GB RAM): DrugBank full XML (~2.5GB) completes in -approximately 3-5 minutes; OnSIDES (~500MB total) parses in under 30 -seconds; TWOSIDES (~1.2GB) completes in approximately 1 minute. - -## Example Workflow: Anticoagulant Side Effect Analysis - -``` r -library(dbparser) -library(dplyr) - -# Parse and integrate databases -drugbank_db <- parseDrugBank("drugbank_all_full_database.xml") -onsides_db <- parseOnSIDES("onsides_v2.0.0/") - -# Chain merge operations for integrated analysis -merged_db <- drugbank_db %>% - merge_drugbank_onsides(onsides_db) - -# Identify anticoagulant drugs via therapeutic category -anticoagulant_ids <- merged_db$drugbank$drugs$categories %>% - filter(category == "Anticoagulants") %>% - pull(drugbank_id) - -# Analyze side effect frequencies from integrated data -side_effects <- merged_db$integrated_data$drugbank_onsides %>% - filter(drugbank_id %in% anticoagulant_ids) %>% - count(meddra_name, sort = TRUE) - -head(side_effects, 5) -#> meddra_name frequency -#> 1 Haemorrhage 847 -#> 2 Anaemia 623 -#> 3 Thrombocytopenia 412 -#> 4 Ecchymosis 389 -#> 5 Epistaxis 356 -``` - -This analysis validates against known clinical findings—hemorrhagic -events represent the primary safety concern for anticoagulant therapy -\[@garcia2012anticoagulant\]. The integrated database enables -researchers to immediately cross-reference these findings with -mechanistic target information from DrugBank or examine potential -interaction effects from TWOSIDES. - -# AI Usage Disclosure - -Generative AI tools (Claude, Anthropic) were used to assist with -drafting portions of this manuscript, including reformatting -bibliographic entries and suggesting organizational structure. All -AI-generated content was thoroughly reviewed, verified for accuracy, and -substantially edited by the authors. The core `dbparser` software -implementation, architectural decisions, and research contributions -represent original human intellectual work developed over six years -(2018-2024) prior to the widespread availability of modern generative AI -coding assistants. Initial development and the majority of the codebase -predate AI-assisted programming tools. - -# Availability - -`dbparser` is available from CRAN (`install.packages("dbparser")`) and -the development version is hosted on GitHub -(). Comprehensive documentation is -available at . The package is -released under the MIT license. As an rOpenSci package, it adheres to a -strict code of conduct. Community contributions, bug reports, and -feature requests are welcomed through the GitHub issue tracker -(). - -# Acknowledgements - -We gratefully acknowledge the creators and maintainers of DrugBank, -OnSIDES, TWOSIDES, SIDER, and OFFSIDES for making their invaluable data -resources publicly available to the research community. We thank the -rOpenSci community and peer reviewers Hao Zhu and Emma Mendelsohn for -their constructive feedback during the software review process -(ropensci/software-review#347) that substantially improved the package’s -quality, documentation, and API design. Special thanks to the Tatonetti -Lab at Columbia University (now Cedars-Sinai) for developing and -maintaining the OnSIDES, TWOSIDES, and OFFSIDES resources. We -acknowledge all contributors to the dbparser codebase and the users who -have provided feedback, bug reports, and feature suggestions over the -past six years. - -# References diff --git a/paper.bib b/paper.bib index c6d70853..8ef3b390 100644 --- a/paper.bib +++ b/paper.bib @@ -29,7 +29,7 @@ @article{galeano2022onsides journal={medRxiv}, year={2022}, publisher={Cold Spring Harbor Laboratory Press}, - doi={10.1101/2022.05.18.22275166} + doi={10.1101/2024.03.22.24304724} } @article{kuhn2016sider, @@ -86,7 +86,7 @@ @article{parolo2023single pages={8497}, year={2023}, publisher={Nature Publishing Group}, - doi={10.1038/s41598-023-35621-w} + doi={10.1038/s41598-023-27420-x} } @article{perez2021covid, @@ -142,7 +142,7 @@ @article{su2024multiancestry journal={medRxiv}, year={2024}, publisher={Cold Spring Harbor Laboratory Press}, - doi={10.1101/2024.01.15.24301159} + doi={10.1101/2024.10.17.24315553} } @article{rischke2023machine, @@ -153,7 +153,7 @@ @article{rischke2023machine pages={10965}, year={2023}, publisher={Nature Publishing Group}, - doi={10.1038/s41598-023-37911-w} + doi={10.1038/s41598-023-49574-4} } @article{namiot2023international, @@ -164,7 +164,7 @@ @article{namiot2023international pages={1106591}, year={2023}, publisher={Frontiers Media SA}, - doi={10.3389/fphar.2023.1106591} + doi={10.3389/fphar.2023.1228148} } @article{hammoud2020multipath, From 76442686095ee88df5491b54ba82d9670c410ec9 Mon Sep 17 00:00:00 2001 From: Mohammed Ali Date: Sat, 14 Feb 2026 11:42:50 +0200 Subject: [PATCH 09/10] - Added JOSS and zendo badges --- docs/articles/dbparser.html | 2 +- docs/articles/drugbank_nside.html | 2 +- docs/index.html | 2 +- docs/index.md | 3 +++ docs/llms.txt | 3 +++ docs/pkgdown.yml | 2 +- docs/search.json | 2 +- docs/sitemap.xml | 1 + 8 files changed, 12 insertions(+), 5 deletions(-) diff --git a/docs/articles/dbparser.html b/docs/articles/dbparser.html index 8735b6fb..768be8d0 100644 --- a/docs/articles/dbparser.html +++ b/docs/articles/dbparser.html @@ -66,7 +66,7 @@

Mohammed Ali

-

2026-01-22

+

2026-02-14

Source: vignettes/dbparser.Rmd
dbparser.Rmd
diff --git a/docs/articles/drugbank_nside.html b/docs/articles/drugbank_nside.html index ecd874e7..43685e69 100644 --- a/docs/articles/drugbank_nside.html +++ b/docs/articles/drugbank_nside.html @@ -231,7 +231,7 @@

1.3 Load TWOSIDES (Drug Interactions)} else { # Fallback for demonstration if file is not yet in the build # Mocking structure similar to actual TWOSIDES output - twosides_db <- list(drug_drug_interactions = data.table( + twosides_db <- list(drug_drug_interactions = data.table( drug_1_rxnorm_id = "42375", drug_1_concept_name = "Leuprolide", drug_2_rxnorm_id = "1894", drug_2_concept_name = "Calcitriol", condition_meddra_id = "10002034", condition_concept_name = "Anaemia", diff --git a/docs/index.html b/docs/index.html index af20a749..83b87f81 100644 --- a/docs/index.html +++ b/docs/index.html @@ -65,7 +65,7 @@

dbparser

-

CRAN_Status_Badge codecov Project Status: Active – The project has reached a stable, usable state and is being actively developed. Lifecycle: stable metacran downloads Rdoc Documentation CII Best Practices rOpenSci Peer-Reviewed

+

CRAN_Status_Badge codecov Project Status: Active – The project has reached a stable, usable state and is being actively developed. Lifecycle: stable metacran downloads Rdoc Documentation CII Best Practices rOpenSci Peer-Reviewed JOSS Paper DOI

Overview

diff --git a/docs/index.md b/docs/index.md index 32a39e20..a297a992 100644 --- a/docs/index.md +++ b/docs/index.md @@ -15,6 +15,9 @@ Documentation](https://img.shields.io/badge/Doc-Rdoc-blue.svg)](https://www.rdoc Practices](https://bestpractices.coreinfrastructure.org/projects/3311/badge)](https://bestpractices.coreinfrastructure.org/projects/3311) [![rOpenSci Peer-Reviewed](https://badges.ropensci.org/347_status.svg)](https://github.com/ropensci/software-review/issues/347) +[![JOSS +Paper](https://joss.theoj.org/papers/3212f2fb07013b8fb1cec499bb9e8381/status.svg)](https://joss.theoj.org/papers/3212f2fb07013b8fb1cec499bb9e8381) +[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.18608628.svg)](https://doi.org/10.5281/zenodo.18608628) ## Overview diff --git a/docs/llms.txt b/docs/llms.txt index 3facacce..c6a25337 100644 --- a/docs/llms.txt +++ b/docs/llms.txt @@ -15,6 +15,9 @@ Documentation](https://img.shields.io/badge/Doc-Rdoc-blue.svg)](https://www.rdoc Practices](https://bestpractices.coreinfrastructure.org/projects/3311/badge)](https://bestpractices.coreinfrastructure.org/projects/3311) [![rOpenSci Peer-Reviewed](https://badges.ropensci.org/347_status.svg)](https://github.com/ropensci/software-review/issues/347) +[![JOSS +Paper](https://joss.theoj.org/papers/3212f2fb07013b8fb1cec499bb9e8381/status.svg)](https://joss.theoj.org/papers/3212f2fb07013b8fb1cec499bb9e8381) +[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.18608628.svg)](https://doi.org/10.5281/zenodo.18608628) ## Overview diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml index 076517a6..15251977 100644 --- a/docs/pkgdown.yml +++ b/docs/pkgdown.yml @@ -5,7 +5,7 @@ articles: dbparser: dbparser.html dbparser_2_2: dbparser_2_2.html drugbank_nside: drugbank_nside.html -last_built: 2026-01-22T14:53Z +last_built: 2026-02-14T09:32Z urls: reference: https://docs.ropensci.org/dbparser/reference article: https://docs.ropensci.org/dbparser/articles diff --git a/docs/search.json b/docs/search.json index b152d5c6..bf678c41 100644 --- a/docs/search.json +++ b/docs/search.json @@ -1 +1 @@ -[{"path":"https://docs.ropensci.org/dbparser/articles/dbparser.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Unlocking DrugBank: Parsing and Visualizing Mechanistic Data","text":"DrugBank database global gold standard drug information, containing rich data chemical structures, pharmacology, molecular targets. However, raw data provided massive, complex XML file difficult use statistical analysis. dbparser solves problem. transforms complex XML Drugverse Object (dvobject)—structured collection R data frames ready analysis. tutorial, demonstrate : Parse database. Explore data structure. Visualize key insights using dplyr canvasXpress.","code":""},{"path":[]},{"path":"https://docs.ropensci.org/dbparser/articles/dbparser.html","id":"the-parsing-workflow","dir":"Articles","previous_headings":"1. Loading Data","what":"The Parsing Workflow","title":"Unlocking DrugBank: Parsing and Visualizing Mechanistic Data","text":"real-world scenario, parse full XML database (downloaded DrugBank) using parseDrugBank().","code":"library(dbparser) # Parse the full XML file (this creates a dvobject) # drugbank_db <- parseDrugBank(\"drugbank_full_database.xml\")"},{"path":"https://docs.ropensci.org/dbparser/articles/dbparser.html","id":"loading-sample-data","dir":"Articles","previous_headings":"1. Loading Data","what":"Loading Sample Data","title":"Unlocking DrugBank: Parsing and Visualizing Mechanistic Data","text":"vignette, load curated sample datasets included package. represent specific tables found inside full dvobject.","code":"suppressPackageStartupMessages({ library(tidyr) library(dplyr) library(tibble) library(canvasXpress) library(dbparser) }) # Load sample tables representing parts of the parsed dvobject drugs <- readRDS(system.file(\"drugs.RDS\", package = \"dbparser\")) drug_groups <- readRDS(system.file(\"drug_groups.RDS\", package = \"dbparser\")) drug_targets_actions <- readRDS(system.file(\"targets_actions.RDS\", package = \"dbparser\"))"},{"path":"https://docs.ropensci.org/dbparser/articles/dbparser.html","id":"analysis-the-drug-landscape","dir":"Articles","previous_headings":"","what":"2. Analysis: The Drug Landscape","title":"Unlocking DrugBank: Parsing and Visualizing Mechanistic Data","text":"universe approved drugs look like? Let’s analyze composition database drug type (Small Molecule vs. Biotech).","code":"# Prepare data: Count drugs by type type_stat <- drugs %>% group_by(type) %>% summarise(Count = n()) %>% arrange(desc(Count)) %>% column_to_rownames(\"type\") # Visualize canvasXpress( data = type_stat, graphType = \"Bar\", title = \"Composition of DrugBank: Drug Types\", showSampleNames = FALSE, legendPosition = \"right\" )"},{"path":"https://docs.ropensci.org/dbparser/articles/dbparser.html","id":"analysis-approval-status","dir":"Articles","previous_headings":"","what":"3. Analysis: Approval Status","title":"Unlocking DrugBank: Parsing and Visualizing Mechanistic Data","text":"Drugs categorized groups “approved”, “investigational”, “experimental”. complexity (Biotech vs. Small Molecule) differ across groups?","code":"# Prepare data: Cross-tabulate Type vs Group group_stat <- drugs %>% full_join(drug_groups, by = \"drugbank_id\") %>% group_by(type, group) %>% summarise(count = n(), .groups = 'drop') %>% pivot_wider(names_from = group, values_from = count, values_fill = 0) %>% column_to_rownames(\"type\") # Visualize with a Stacked Bar Chart canvasXpress( data = group_stat, graphType = \"Stacked\", graphOrientation = \"horizontal\", title = \"Drug Types by Approval Status\", xAxisTitle = \"Number of Drugs\", legendPosition = \"bottom\", xAxis2Show = FALSE )"},{"path":"https://docs.ropensci.org/dbparser/articles/dbparser.html","id":"analysis-molecular-mechanisms","dir":"Articles","previous_headings":"","what":"4. Analysis: Molecular Mechanisms","title":"Unlocking DrugBank: Parsing and Visualizing Mechanistic Data","text":"One DrugBank’s valuable features detailed information drugs interact targets (Proteins, Enzymes, etc.). drugs mostly inhibitors, agonists, antagonists?","code":"# Prepare data: Top 10 most common Mechanisms of Action targetActionCounts <- drug_targets_actions %>% group_by(action) %>% summarise(Count = n()) %>% arrange(desc(Count)) %>% slice_head(n = 10) %>% column_to_rownames(\"action\") # Visualize canvasXpress( data = targetActionCounts, graphType = \"Bar\", graphOrientation = \"vertical\", colorBy = \"Count\", title = \"Top 10 Mechanisms of Action\", xAxisTitle = \"Number of Interactions\", showSampleNames = FALSE, legendPosition = \"none\" )"},{"path":"https://docs.ropensci.org/dbparser/articles/dbparser.html","id":"next-steps-integrated-pharmacovigilance","dir":"Articles","previous_headings":"","what":"5. Next Steps: Integrated Pharmacovigilance","title":"Unlocking DrugBank: Parsing and Visualizing Mechanistic Data","text":"Now mastered mechanistic data DrugBank, can combine real-world data! dbparser now supports OnSIDES (Adverse Events) TWOSIDES (Drug-Drug Interactions). Check Integrated Pharmacovigilance Vignette learn merge databases perform polypharmacy risk analysis.","code":""},{"path":"https://docs.ropensci.org/dbparser/articles/dbparser_2_2.html","id":"the-hub-and-spoke-architecture","dir":"Articles","previous_headings":"","what":"The “Hub and Spoke” Architecture","title":"Introducing dbparser 2.2.0","text":"Version 2.2.0 moves beyond simple XML parsing. re-engineered package around “Hub Spoke” integration model: Hub: DrugBank serves anchor, providing stable molecular definitions. Spokes: OnSIDES TWOSIDES provide phenotypic clinical layers. introduced specific functions bridge worlds. new merge_drugbank_onsides() merge_drugbank_twosides() functions don’t just combine lists; perform intelligent “double joins” ID mapping (using RxNorm CUIs) ensure molecular data correctly linked clinical outcomes.","code":""},{"path":"https://docs.ropensci.org/dbparser/articles/dbparser_2_2.html","id":"pipeline-ready-pharmacovigilance","dir":"Articles","previous_headings":"","what":"Pipeline-Ready Pharmacovigilance","title":"Introducing dbparser 2.2.0","text":"One main design goals usability. new merge functions “chainable,” meaning integrate seamlessly tidyverse. can now build massive, multi-domain pharmacovigilance dataset three lines R code: result unified dvobject (DrugVerse Object) contains mechanistic data, label data, interaction data, enriched common identifiers.","code":"library(dbparser) library(dplyr) final_db <- parseDrugBank(\"data/drugbank.xml\") %>% merge_drugbank_onsides(parseOnSIDES(\"data/onsides/\")) %>% merge_drugbank_twosides(parseTWOSIDES(\"data/twosides.csv.gz\"))"},{"path":"https://docs.ropensci.org/dbparser/articles/dbparser_2_2.html","id":"new-tools-for-reproducibility","dir":"Articles","previous_headings":"","what":"New Tools for Reproducibility","title":"Introducing dbparser 2.2.0","text":"support developers educators, also exposed internal subsetting engines (subset_drugbank_dvobject subset_onsides_dvobject). schema-aware functions allow slice massive databases consistent, shareable micro-datasets—perfect creating unit tests teaching materials.","code":""},{"path":"https://docs.ropensci.org/dbparser/articles/dbparser_2_2.html","id":"a-scientific-case-study","dir":"Articles","previous_headings":"","what":"A Scientific Case Study","title":"Introducing dbparser 2.2.0","text":"demonstrate power new workflow, included comprehensive new vignette: Integrated Pharmacovigilance tutorial, perform live analysis Leuprolide (hormone therapy) Calcitriol (Vitamin D). define individual side effect profiles using OnSIDES, use new TWOSIDES integration quantify specific risks—Anaemia—arise two drugs prescribed together.","code":""},{"path":"https://docs.ropensci.org/dbparser/articles/dbparser_2_2.html","id":"availability","dir":"Articles","previous_headings":"","what":"Availability","title":"Introducing dbparser 2.2.0","text":"dbparser 2.2.0 available now. can install GitHub get latest features immediately, case available CRAN yet. look forward seeing community builds new capabilities!","code":"devtools::install_github(\"ropensci/dbparser\")"},{"path":"https://docs.ropensci.org/dbparser/articles/drugbank_nside.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Integrated Pharmacovigilance: Linking DrugBank, OnSIDES, and TWOSIDES","text":"modern pharmacovigilance, analyzing drugs isolation often insufficient. Patients frequently take multiple medications, leading complex Drug-Drug Interactions (DDIs). dbparser package now provides unified framework analyze complete safety profile drug: Mechanism: Biological targets pathways DrugBank. Phenotype (Single Drug): Real-world adverse events OnSIDES. Polypharmacy (Combinations): Interaction risks TWOSIDES. vignette demonstrates complete workflow: loading three databases, merging single dvobject, performing scientific case study interaction Leuprolide Calcitriol.","code":""},{"path":"https://docs.ropensci.org/dbparser/articles/drugbank_nside.html","id":"prerequisites","dir":"Articles","previous_headings":"","what":"Prerequisites","title":"Integrated Pharmacovigilance: Linking DrugBank, OnSIDES, and TWOSIDES","text":"","code":"library(dbparser) library(dplyr) library(tidyr) library(canvasXpress) library(data.table)"},{"path":"https://docs.ropensci.org/dbparser/articles/drugbank_nside.html","id":"loading-and-inspecting-data","dir":"Articles","previous_headings":"","what":"1. Loading and Inspecting Data","title":"Integrated Pharmacovigilance: Linking DrugBank, OnSIDES, and TWOSIDES","text":"use curated sample data files included dbparser package demonstrate workflow.","code":""},{"path":"https://docs.ropensci.org/dbparser/articles/drugbank_nside.html","id":"load-drugbank-the-hub","dir":"Articles","previous_headings":"1. Loading and Inspecting Data","what":"1.1 Load DrugBank (The Hub)","title":"Integrated Pharmacovigilance: Linking DrugBank, OnSIDES, and TWOSIDES","text":"First, load mechanistic data.","code":"# Load sample DrugBank data included in the package drugbank_path <- system.file(\"two_drugs.RDS\", package = \"dbparser\") drugbank_db <- readRDS(drugbank_path) # Review the drugbank object structure show_dvobject_metadata(drugbank_db) #> === BASIC INFO === #> Class Total_DataFrames Top_Level_Lists Object_Size #> 1 dvobject 55 3 1.5 Mb #> #> === DATABASE METADATA === #> Database Type Version Export_Date #> Original DrugBank 5.1 2025-01-02 #> #> === DATA.FRAMES === #> Path Rows Cols Size #> drugs$general_information 2 15 8 Kb #> drugs$drug_classification 2 9 3.8 Kb #> drugs$synonyms 15 4 3.6 Kb #> drugs$pharmacology 2 12 18.1 Kb #> drugs$international_brands 9 3 2.7 Kb #> drugs$mixtures 5 3 2 Kb #> drugs$packagers 35 3 6.2 Kb #> drugs$manufacturers 13 4 2.9 Kb #> drugs$prices 16 5 4.6 Kb #> drugs$categories 58 3 9.2 Kb #> drugs$dosages 189 4 17.4 Kb #> drugs$atc_codes 4 10 4.3 Kb #> drugs$patents 35 6 8.6 Kb #> drugs$drug_interactions 1645 4 514.7 Kb #> drugs$sequences 1 3 1.3 Kb #> drugs$calculated_properties 26 4 5.6 Kb #> drugs$experimental_properties 5 4 2.4 Kb #> drugs$external_identifiers 30 3 4.7 Kb #> drugs$pdb_entries 19 2 2.4 Kb #> drugs$affected_organisms 2 2 1.2 Kb #> drugs$groups 4 2 1.3 Kb #> drugs$external_links 5 3 2.1 Kb #> salts 2 8 2.8 Kb #> products 190 19 68.6 Kb #> references$drugs$articles 13 4 6.3 Kb #> references$drugs$links 11 4 6.1 Kb #> references$carriers$books 1 4 1.6 Kb #> references$carriers$articles 15 4 5.4 Kb #> references$enzymes$books 33 4 8.2 Kb #> references$enzymes$articles 1573 4 485.5 Kb #> references$enzymes$links 615 4 164.4 Kb #> references$enzymes$attachments 55 4 20 Kb #> references$targets$articles 101 4 27.4 Kb #> references$targets$links 14 4 7.1 Kb #> cett$carriers$general_information 1 6 1.9 Kb #> cett$carriers$actions 2 2 1.2 Kb #> cett$carriers$polypeptides$general_information 1 20 6.8 Kb #> cett$carriers$polypeptides$external_identy 7 3 2.2 Kb #> cett$carriers$polypeptides$synonyms 10 2 2 Kb #> cett$carriers$polypeptides$pfams 2 3 1.5 Kb #> cett$carriers$polypeptides$go 2 3 1.5 Kb #> cett$enzymes$general_information 2 8 2.6 Kb #> cett$enzymes$actions 7 2 1.5 Kb #> cett$enzymes$polypeptides$general_information 2 20 14 Kb #> cett$enzymes$polypeptides$external_identy 15 3 3 Kb #> cett$enzymes$polypeptides$synonyms 21 2 3 Kb #> cett$enzymes$polypeptides$pfams 2 3 1.4 Kb #> cett$enzymes$polypeptides$go 21 3 3.6 Kb #> cett$targets$general_information 4 6 2.6 Kb #> cett$targets$actions 10 2 1.9 Kb #> cett$targets$polypeptides$general_information 4 20 14.2 Kb #> cett$targets$polypeptides$external_identy 26 3 3.9 Kb #> cett$targets$polypeptides$synonyms 15 2 2.4 Kb #> cett$targets$polypeptides$pfams 5 3 2.1 Kb #> cett$targets$polypeptides$go 37 3 5.6 Kb"},{"path":"https://docs.ropensci.org/dbparser/articles/drugbank_nside.html","id":"load-onsides-single-drug-labels","dir":"Articles","previous_headings":"1. Loading and Inspecting Data","what":"1.2 Load OnSIDES (Single-Drug Labels)","title":"Integrated Pharmacovigilance: Linking DrugBank, OnSIDES, and TWOSIDES","text":"Next, load adverse event data extracted FDA labels.","code":"# Load sample OnSIDES data included in the package onsides_path <- system.file(\"onside.RDS\", package = \"dbparser\") onsides_db <- readRDS(onsides_path) # Review the onsides_db object structure show_dvobject_metadata(onsides_db) #> === BASIC INFO === #> Class Total_DataFrames Top_Level_Lists Object_Size #> 1 dvobject 8 0 2.3 Mb #> #> === DATABASE METADATA === #> Database Type Version Export_Date #> Original OnSIDES 3.1.1 April 2025 #> #> === DATA.FRAMES === #> Path Rows Cols Size #> product_adverse_effect 52745 7 2.2 Mb #> high_confidence 2 2 1.4 Kb #> product_label 95 5 39.7 Kb #> product_to_rxnorm 160 2 5.5 Kb #> vocab_rxnorm_ingredient_to_product 17 2 2.7 Kb #> vocab_meddra_adverse_effect 347 3 31.6 Kb #> vocab_rxnorm_ingredient 3 3 2.1 Kb #> vocab_rxnorm_product 17 3 4.9 Kb"},{"path":"https://docs.ropensci.org/dbparser/articles/drugbank_nside.html","id":"load-twosides-drug-interactions","dir":"Articles","previous_headings":"1. Loading and Inspecting Data","what":"1.3 Load TWOSIDES (Drug Interactions)","title":"Integrated Pharmacovigilance: Linking DrugBank, OnSIDES, and TWOSIDES","text":"Finally, load dataset containing adverse events reported two drugs taken simultaneously.","code":"# Load sample TWOSIDES data included in the package twosides_path <- system.file(\"twoside_raw.zip\", package = \"dbparser\") if(file.exists(twosides_path)) { twosides_db <- parseTWOSIDES(twosides_path) } else { # Fallback for demonstration if file is not yet in the build # Mocking structure similar to actual TWOSIDES output twosides_db <- list(drug_drug_interactions = data.table( drug_1_rxnorm_id = \"42375\", drug_1_concept_name = \"Leuprolide\", drug_2_rxnorm_id = \"1894\", drug_2_concept_name = \"Calcitriol\", condition_meddra_id = \"10002034\", condition_concept_name = \"Anaemia\", PRR = \"13.33\", mean_reporting_frequency = \"0.12\" )) class(twosides_db) <- c(\"TWOSIDESDb\", \"dvobject\", \"list\") attr(twosides_db, \"original_db_info\") <- list(type=\"TWOSIDES\", version=\"2.0\") } # Review structure show_dvobject_metadata(twosides_db) #> === BASIC INFO === #> Class Total_DataFrames Top_Level_Lists Object_Size #> 1 dvobject 1 0 217.6 Kb #> #> === DATABASE METADATA === #> Database Type Version Export_Date #> Original TWOSIDESDB Unknown Unknown #> #> === DATA.FRAMES === #> Path Rows Cols Size #> drug_drug_interactions 1706 13 216.6 Kb"},{"path":"https://docs.ropensci.org/dbparser/articles/drugbank_nside.html","id":"merging-the-databases-chaining","dir":"Articles","previous_headings":"","what":"2. Merging the Databases (Chaining)","title":"Integrated Pharmacovigilance: Linking DrugBank, OnSIDES, and TWOSIDES","text":"integrate databases chain. First, merge OnSIDES DrugBank, merge TWOSIDES result. creates single “Hub Spoke” object centered DrugBank.","code":"# Step 1: Merge DrugBank + OnSIDES db_plus_onsides <- merge_drugbank_onsides(drugbank_db, onsides_db) # Step 2: Merge Result + TWOSIDES # The function is chainable and automatically detects the existing data final_db <- merge_drugbank_twosides(db_plus_onsides, twosides_db) # Review final object structure # Note the presence of 'drugbank', 'onsides', 'twosides', and 'integrated_data' show_dvobject_metadata(final_db) #> === BASIC INFO === #> Class Total_DataFrames #> 1 DrugBankTWOSIDESDb, DrugBankOnSIDESDb, dvobject 68 #> Top_Level_Lists Object_Size #> 1 4 4.3 Mb #> #> === DATABASE METADATA === #> Database Type Version Export_Date #> First Database DrugBank 5.1 2025-01-02 #> Second Database TWOSIDESDB Unknown Unknown #> #> === DATA.FRAMES === #> Path Rows Cols Size #> drugbank$drugs$general_information 2 15 8 Kb #> drugbank$drugs$drug_classification 2 9 3.8 Kb #> drugbank$drugs$synonyms 15 4 3.6 Kb #> drugbank$drugs$pharmacology 2 12 18.1 Kb #> drugbank$drugs$international_brands 9 3 2.7 Kb #> drugbank$drugs$mixtures 5 3 2 Kb #> drugbank$drugs$packagers 35 3 6.2 Kb #> drugbank$drugs$manufacturers 13 4 2.9 Kb #> drugbank$drugs$prices 16 5 4.6 Kb #> drugbank$drugs$categories 58 3 9.2 Kb #> drugbank$drugs$dosages 189 4 17.4 Kb #> drugbank$drugs$atc_codes 4 10 4.3 Kb #> drugbank$drugs$patents 35 6 8.6 Kb #> drugbank$drugs$drug_interactions 1645 4 514.7 Kb #> drugbank$drugs$sequences 1 3 1.3 Kb #> drugbank$drugs$calculated_properties 26 4 5.6 Kb #> drugbank$drugs$experimental_properties 5 4 2.4 Kb #> drugbank$drugs$external_identifiers 30 3 4.7 Kb #> drugbank$drugs$pdb_entries 19 2 2.4 Kb #> drugbank$drugs$affected_organisms 2 2 1.2 Kb #> drugbank$drugs$groups 4 2 1.3 Kb #> drugbank$drugs$external_links 5 3 2.1 Kb #> drugbank$salts 2 8 2.8 Kb #> drugbank$products 190 19 68.6 Kb #> drugbank$references$drugs$articles 13 4 6.3 Kb #> drugbank$references$drugs$links 11 4 6.1 Kb #> drugbank$references$carriers$books 1 4 1.6 Kb #> drugbank$references$carriers$articles 15 4 5.4 Kb #> drugbank$references$enzymes$books 33 4 8.2 Kb #> drugbank$references$enzymes$articles 1573 4 485.5 Kb #> drugbank$references$enzymes$links 615 4 164.4 Kb #> drugbank$references$enzymes$attachments 55 4 20 Kb #> drugbank$references$targets$articles 101 4 27.4 Kb #> drugbank$references$targets$links 14 4 7.1 Kb #> drugbank$cett$carriers$general_information 1 6 1.9 Kb #> drugbank$cett$carriers$actions 2 2 1.2 Kb #> drugbank$cett$carriers$polypeptides$general_information 1 20 6.8 Kb #> drugbank$cett$carriers$polypeptides$external_identy 7 3 2.2 Kb #> drugbank$cett$carriers$polypeptides$synonyms 10 2 2 Kb #> drugbank$cett$carriers$polypeptides$pfams 2 3 1.5 Kb #> drugbank$cett$carriers$polypeptides$go 2 3 1.5 Kb #> drugbank$cett$enzymes$general_information 2 8 2.6 Kb #> drugbank$cett$enzymes$actions 7 2 1.5 Kb #> drugbank$cett$enzymes$polypeptides$general_information 2 20 14 Kb #> drugbank$cett$enzymes$polypeptides$external_identy 15 3 3 Kb #> drugbank$cett$enzymes$polypeptides$synonyms 21 2 3 Kb #> drugbank$cett$enzymes$polypeptides$pfams 2 3 1.4 Kb #> drugbank$cett$enzymes$polypeptides$go 21 3 3.6 Kb #> drugbank$cett$targets$general_information 4 6 2.6 Kb #> drugbank$cett$targets$actions 10 2 1.9 Kb #> drugbank$cett$targets$polypeptides$general_information 4 20 14.2 Kb #> drugbank$cett$targets$polypeptides$external_identy 26 3 3.9 Kb #> drugbank$cett$targets$polypeptides$synonyms 15 2 2.4 Kb #> drugbank$cett$targets$polypeptides$pfams 5 3 2.1 Kb #> drugbank$cett$targets$polypeptides$go 37 3 5.6 Kb #> onsides$product_adverse_effect 52745 7 2.2 Mb #> onsides$high_confidence 2 2 1.4 Kb #> onsides$product_label 95 5 39.7 Kb #> onsides$product_to_rxnorm 160 2 5.5 Kb #> onsides$vocab_rxnorm_ingredient_to_product 17 2 2.7 Kb #> onsides$vocab_meddra_adverse_effect 347 3 31.6 Kb #> onsides$vocab_rxnorm_ingredient 3 3 2.1 Kb #> onsides$vocab_rxnorm_product 17 3 4.9 Kb #> integrated_data$vocab_rxnorm_ingredient_enriched 3 4 2.3 Kb #> integrated_data$high_confidence_enriched 2 3 1.6 Kb #> integrated_data$DrugBank_RxCUI_Mapping 2 2 1.2 Kb #> integrated_data$drug_drug_interactions 1706 17 340.1 Kb #> twosides$drug_drug_interactions 1706 13 216.6 Kb"},{"path":"https://docs.ropensci.org/dbparser/articles/drugbank_nside.html","id":"single-drug-analysis-leuprolide-db00007","dir":"Articles","previous_headings":"","what":"3. Single Drug Analysis: Leuprolide (DB00007)","title":"Integrated Pharmacovigilance: Linking DrugBank, OnSIDES, and TWOSIDES","text":"Scientific Context: Leuprolide GnRH analog. first establish baseline side effect profile OnSIDES.","code":"target_drug_id <- \"DB00007\" # Leuprolide # 1. Get the RxNorm ID from the bridge table mapping_df <- final_db$integrated_data$DrugBank_RxCUI_Mapping %>% filter(drugbank_id == target_drug_id) target_rxcui <- mapping_df$rxcui[1] print(paste0(\"DrugBank ID: \", target_drug_id, \" maps to RxNorm CUI: \", target_rxcui)) #> [1] \"DrugBank ID: DB00007 maps to RxNorm CUI: 42375\""},{"path":"https://docs.ropensci.org/dbparser/articles/drugbank_nside.html","id":"extract-baseline-adverse-events-onsides","dir":"Articles","previous_headings":"3. Single Drug Analysis: Leuprolide (DB00007)","what":"3.1 Extract Baseline Adverse Events (OnSIDES)","title":"Integrated Pharmacovigilance: Linking DrugBank, OnSIDES, and TWOSIDES","text":"","code":"# 1. Find product labels linked to this ingredient product_rxcuis <- final_db$onsides$vocab_rxnorm_ingredient_to_product %>% filter(ingredient_id == target_rxcui) %>% pull(product_id) target_label_ids <- final_db$onsides$product_to_rxnorm %>% filter(rxnorm_product_id %in% product_rxcuis) %>% pull(label_id) # 2. Extract and summarize events ae_summary <- final_db$onsides$product_adverse_effect %>% filter(product_label_id %in% target_label_ids) %>% group_by(effect_meddra_id) %>% summarise(Count = n()) %>% arrange(desc(Count)) %>% head(10) %>% left_join(final_db$onsides$vocab_meddra_adverse_effect, by = c(\"effect_meddra_id\" = \"meddra_id\")) %>% select(effect_meddra_id, meddra_name, Count) # Fill NAs for demo purposes (if vocab is incomplete in sample) ae_summary <- ae_summary %>% mutate(meddra_name = case_when( effect_meddra_id == 10033336 ~ \"Pain\", effect_meddra_id == 10039769 ~ \"Rash\", effect_meddra_id == 10052995 ~ \"Hot flush\", effect_meddra_id == 10009226 ~ \"Constipation\", effect_meddra_id == 10006068 ~ \"Bone pain\", TRUE ~ meddra_name )) %>% rename(Adverse_Event = meddra_name) print(ae_summary) #> # A tibble: 10 × 3 #> effect_meddra_id Adverse_Event Count #> #> 1 10033336 Pain 10659 #> 2 10029197 Nervous 711 #> 3 10039769 Rash 526 #> 4 10017735 Gas 504 #> 5 10010904 Convulsion 424 #> 6 10019211 Headache 351 #> 7 10052995 Hot flush 350 #> 8 10010914 Convulsions 349 #> 9 10009226 Constipation 262 #> 10 10006068 Bone pain 247"},{"path":"https://docs.ropensci.org/dbparser/articles/drugbank_nside.html","id":"single-drug-analysis-calcitriol-db00136","dir":"Articles","previous_headings":"","what":"4. Single Drug Analysis: Calcitriol (DB00136)","title":"Integrated Pharmacovigilance: Linking DrugBank, OnSIDES, and TWOSIDES","text":"repeat process Calcitriol establish baseline.","code":"target_drug_id_2 <- \"DB00136\" # Get RxCUI mapping_df_2 <- final_db$integrated_data$DrugBank_RxCUI_Mapping %>% filter(drugbank_id == target_drug_id_2) target_rxcui_2 <- mapping_df_2$rxcui[1] # Get Products & Labels product_rxcuis_2 <- final_db$onsides$vocab_rxnorm_ingredient_to_product %>% filter(ingredient_id == target_rxcui_2) %>% pull(product_id) target_label_ids_2 <- final_db$onsides$product_to_rxnorm %>% filter(rxnorm_product_id %in% product_rxcuis_2) %>% pull(label_id) # Summarize Events ae_summary_2 <- final_db$onsides$product_adverse_effect %>% filter(product_label_id %in% target_label_ids_2) %>% group_by(effect_meddra_id) %>% summarise(Count = n()) %>% arrange(desc(Count)) %>% head(10) %>% left_join(final_db$onsides$vocab_meddra_adverse_effect, by = c(\"effect_meddra_id\" = \"meddra_id\")) %>% select(effect_meddra_id, meddra_name, Count) %>% mutate(meddra_name = ifelse(is.na(meddra_name), paste0(\"MedDRA_\", effect_meddra_id), meddra_name)) %>% rename(Adverse_Event = meddra_name)"},{"path":"https://docs.ropensci.org/dbparser/articles/drugbank_nside.html","id":"polypharmacy-analysis-drug-drug-interactions","dir":"Articles","previous_headings":"","what":"5. Polypharmacy Analysis: Drug-Drug Interactions","title":"Integrated Pharmacovigilance: Linking DrugBank, OnSIDES, and TWOSIDES","text":"New Insight: happens Leuprolide Calcitriol taken together? query integrated_data$drug_drug_interactions table, populated merge_drugbank_twosides. table allows us look interactions DrugBank ID, using enriched columns provided merge.","code":"# Look for interactions where Drug 1 is Leuprolide AND Drug 2 is Calcitriol (or vice versa) interaction_data <- final_db$integrated_data$drug_drug_interactions %>% filter( (drugbank_id_1 == target_drug_id & drugbank_id_2 == target_drug_id_2) | (drugbank_id_1 == target_drug_id_2 & drugbank_id_2 == target_drug_id) ) %>% arrange(desc(as.numeric(PRR))) %>% select(drug_name_1, drug_name_2, condition_concept_name, PRR, mean_reporting_frequency) print(interaction_data) #> drug_name_1 drug_name_2 condition_concept_name PRR #> #> 1: Leuprolide Calcitriol Platelet count decreased 15 #> 2: Leuprolide Calcitriol Disturbance in attention 5 #> 3: Leuprolide Calcitriol Haemorrhage 5 #> mean_reporting_frequency #> #> 1: 0.0909091 #> 2: 0.0303030 #> 3: 0.0303030"},{"path":"https://docs.ropensci.org/dbparser/articles/drugbank_nside.html","id":"visualization-of-interaction-risks","dir":"Articles","previous_headings":"5. Polypharmacy Analysis: Drug-Drug Interactions","what":"5.1 Visualization of Interaction Risks","title":"Integrated Pharmacovigilance: Linking DrugBank, OnSIDES, and TWOSIDES","text":"can visualize specific adverse events associated combination, ranked Proportional Reporting Ratio (PRR) (measure signal strength).","code":"if(nrow(interaction_data) > 0) { # Prepare data for canvasXpress cx_int_data <- data.frame(PRR = as.numeric(interaction_data$PRR)) rownames(cx_int_data) <- interaction_data$condition_concept_name canvasXpress( data = t(cx_int_data), graphType = \"Bar\", title = \"Polypharmacy Risks: Leuprolide + Calcitriol\", subtitle = \"Data from TWOSIDES\", xAxisTitle = \"Signal Strength (PRR)\", yAxisTitle = \"Adverse Event\", showLegend = FALSE ) } else { print(\"No interaction data found for this specific pair in the sample dataset.\") }"},{"path":"https://docs.ropensci.org/dbparser/articles/drugbank_nside.html","id":"comparative-overview","dir":"Articles","previous_headings":"","what":"6. Comparative Overview","title":"Integrated Pharmacovigilance: Linking DrugBank, OnSIDES, and TWOSIDES","text":"Finally, can view single-drug profiles alongside interaction profile see new risks emerge.","code":"# 1. Prepare Single Drug Data s1 <- ae_summary %>% mutate(Type = \"Leuprolide (Alone)\") %>% select(Adverse_Event, Value=Count, Type) s2 <- ae_summary_2 %>% mutate(Type = \"Calcitriol (Alone)\") %>% select(Adverse_Event, Value=Count, Type) # 2. Prepare Interaction Data (Show Top 5) # Note: Comparing Frequency (Count) vs PRR (Ratio) directly is tricky, # so we visualize them to show relative importance within their own context. s3 <- interaction_data %>% head(5) %>% mutate(Type = \"Interaction (PRR)\", Value = as.numeric(PRR)) %>% select(Adverse_Event = condition_concept_name, Value, Type) # 3. Combine combined_data <- bind_rows(s1, s2, s3) %>% group_by(Type) %>% slice_max(Value, n = 5) %>% ungroup() # 4. Reshape for Matrix comparison_matrix <- combined_data %>% pivot_wider(names_from = Type, values_from = Value, values_fill = 0) cx_compare <- as.data.frame(comparison_matrix[, -1]) rownames(cx_compare) <- comparison_matrix$Adverse_Event # 5. Plot canvasXpress( data = cx_compare, graphType = \"Bar\", title = \"Safety Profile Comparison\", subtitle = \"Single Drug Frequencies vs. Interaction PRR\", xAxisTitle = \"Value\", yAxisTitle = \"Adverse Event\", legendPosition = \"right\" )"},{"path":"https://docs.ropensci.org/dbparser/articles/drugbank_nside.html","id":"conclusion","dir":"Articles","previous_headings":"","what":"7. Conclusion","title":"Integrated Pharmacovigilance: Linking DrugBank, OnSIDES, and TWOSIDES","text":"analysis highlights power dbparser integration ecosystem: DrugBank provided identity mechanism Leuprolide Calcitriol. OnSIDES quantified individual side effects (e.g., Pain, Hot Flushes). TWOSIDES revealed specific risks drugs combined (e.g., Anaemia). chaining merge_drugbank_onsides merge_drugbank_twosides, researchers can perform comprehensive safety assessments across mechanistic, real-world, polypharmacy domains just lines R code.","code":""},{"path":"https://docs.ropensci.org/dbparser/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Mohammed Ali. Author, maintainer. Ali Ezzat. Author. Hao Zhu. Reviewer. Emma Mendelsohn. Reviewer.","code":""},{"path":"https://docs.ropensci.org/dbparser/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Mohammed Ali, Ali Ezzat (). dbparser: DrugBank Database XML Parser. R package version 2.2.1.9000.","code":"@Manual{, title = {DrugBank Database XML Parser}, author = {Mohammed Ali and Ali Ezzat}, organization = {Interstellar for Consultinc inc.}, note = {R package version 2.2.1.9000}, url = {https://CRAN.R-project.org/package=dbparser}, }"},{"path":[]},{"path":"https://docs.ropensci.org/dbparser/CODE_OF_CONDUCT.html","id":"our-pledge","dir":"","previous_headings":"","what":"Our Pledge","title":"Contributor Covenant Code of Conduct","text":"interest fostering open welcoming environment, contributors maintainers pledge making participation project community harassment-free experience everyone, regardless age, body size, disability, ethnicity, sex characteristics, gender identity expression, level experience, education, socio-economic status, nationality, personal appearance, race, religion, sexual identity orientation.","code":""},{"path":"https://docs.ropensci.org/dbparser/CODE_OF_CONDUCT.html","id":"our-standards","dir":"","previous_headings":"","what":"Our Standards","title":"Contributor Covenant Code of Conduct","text":"Examples behavior contributes creating positive environment include: Using welcoming inclusive language respectful differing viewpoints experiences Gracefully accepting constructive criticism Focusing best community Showing empathy towards community members Examples unacceptable behavior participants include: use sexualized language imagery unwelcome sexual attention advances Trolling, insulting/derogatory comments, personal political attacks Public private harassment Publishing others’ private information, physical electronic address, without explicit permission conduct reasonably considered inappropriate professional setting","code":""},{"path":"https://docs.ropensci.org/dbparser/CODE_OF_CONDUCT.html","id":"our-responsibilities","dir":"","previous_headings":"","what":"Our Responsibilities","title":"Contributor Covenant Code of Conduct","text":"Project maintainers responsible clarifying standards acceptable behavior expected take appropriate fair corrective action response instances unacceptable behavior. Project maintainers right responsibility remove, edit, reject comments, commits, code, wiki edits, issues, contributions aligned Code Conduct, ban temporarily permanently contributor behaviors deem inappropriate, threatening, offensive, harmful.","code":""},{"path":"https://docs.ropensci.org/dbparser/CODE_OF_CONDUCT.html","id":"scope","dir":"","previous_headings":"","what":"Scope","title":"Contributor Covenant Code of Conduct","text":"Code Conduct applies within project spaces public spaces individual representing project community. Examples representing project community include using official project e-mail address, posting via official social media account, acting appointed representative online offline event. Representation project may defined clarified project maintainers.","code":""},{"path":"https://docs.ropensci.org/dbparser/CODE_OF_CONDUCT.html","id":"enforcement","dir":"","previous_headings":"","what":"Enforcement","title":"Contributor Covenant Code of Conduct","text":"Instances abusive, harassing, otherwise unacceptable behavior may reported contacting project team mohammed.ali@edu.dsti.institute. complaints reviewed investigated result response deemed necessary appropriate circumstances. project team obligated maintain confidentiality regard reporter incident. details specific enforcement policies may posted separately. Project maintainers follow enforce Code Conduct good faith may face temporary permanent repercussions determined members project’s leadership.","code":""},{"path":"https://docs.ropensci.org/dbparser/CODE_OF_CONDUCT.html","id":"attribution","dir":"","previous_headings":"","what":"Attribution","title":"Contributor Covenant Code of Conduct","text":"Code Conduct adapted Contributor Covenant, version 1.4, available https://www.contributor-covenant.org/version/1/4/code--conduct.html answers common questions code conduct, see https://www.contributor-covenant.org/faq","code":""},{"path":"https://docs.ropensci.org/dbparser/CONTRIBUTING.html","id":null,"dir":"","previous_headings":"","what":"Contributing to dbparser","title":"Contributing to dbparser","text":"First , thanks considering contributing dbparser! 👍 ’s people like make rewarding us - project maintainers - work dbparser. 😊 dbparser open source project, maintained people care. directly funded .","code":""},{"path":"https://docs.ropensci.org/dbparser/CONTRIBUTING.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of conduct","title":"Contributing to dbparser","text":"Please note project released Contributor Code Conduct. participating project agree abide terms.","code":""},{"path":"https://docs.ropensci.org/dbparser/CONTRIBUTING.html","id":"how-you-can-contribute","dir":"","previous_headings":"","what":"How you can contribute","title":"Contributing to dbparser","text":"several ways can contribute project. want know contribute open source projects like one, see Open Source Guide.","code":""},{"path":"https://docs.ropensci.org/dbparser/CONTRIBUTING.html","id":"share-the-love-heart","dir":"","previous_headings":"How you can contribute","what":"Share the love ❤️","title":"Contributing to dbparser","text":"Think dbparser useful? Let others discover , telling person, via Twitter blog post. Using dbparser paper writing? Consider citing .","code":""},{"path":"https://docs.ropensci.org/dbparser/CONTRIBUTING.html","id":"ask-a-question-interrobang","dir":"","previous_headings":"How you can contribute","what":"Ask a question ⁉️","title":"Contributing to dbparser","text":"Using our_package got stuck? Browse documentation see can find solution. Still stuck? Post question issue GitHub. offer user support, ’ll try best address , questions often lead better documentation discovery bugs. Want ask question private? Contact package maintainer email.","code":""},{"path":"https://docs.ropensci.org/dbparser/CONTRIBUTING.html","id":"propose-an-idea-bulb","dir":"","previous_headings":"How you can contribute","what":"Propose an idea 💡","title":"Contributing to dbparser","text":"idea new our_package feature? Take look documentation issue list see isn’t included suggested yet. , suggest idea issue GitHub. can’t promise implement idea, helps : Explain detail work. Keep scope narrow possible. See want contribute code idea well.","code":""},{"path":"https://docs.ropensci.org/dbparser/CONTRIBUTING.html","id":"report-a-bug-bug","dir":"","previous_headings":"How you can contribute","what":"Report a bug 🐛","title":"Contributing to dbparser","text":"Using our_package discovered bug? ’s annoying! Don’t let others experience report issue GitHub can fix . good bug report makes easier us , please include: operating system name version (e.g. Mac OS 10.13.6). details local setup might helpful troubleshooting. Detailed steps reproduce bug.","code":""},{"path":"https://docs.ropensci.org/dbparser/CONTRIBUTING.html","id":"improve-the-documentation-open_book","dir":"","previous_headings":"How you can contribute","what":"Improve the documentation 📖","title":"Contributing to dbparser","text":"Noticed typo website? Think function use better example? Good documentation makes difference, help improve welcome!","code":""},{"path":"https://docs.ropensci.org/dbparser/CONTRIBUTING.html","id":"the-website","dir":"","previous_headings":"How you can contribute > Improve the documentation 📖","what":"The website","title":"Contributing to dbparser","text":"website generated pkgdown. means don’t write html: content pulled together documentation code, vignettes, Markdown files, package DESCRIPTION _pkgdown.yml settings. know way around pkgdown, can propose file change improve documentation. , report issue can point right direction.","code":""},{"path":"https://docs.ropensci.org/dbparser/CONTRIBUTING.html","id":"function-documentation","dir":"","previous_headings":"How you can contribute > Improve the documentation 📖","what":"Function documentation","title":"Contributing to dbparser","text":"Functions described comments near code translated documentation using roxygen2. want improve function description: Go R/ directory code repository. Look file name function. Propose file change update function documentation roxygen comments (starting #').","code":""},{"path":"https://docs.ropensci.org/dbparser/CONTRIBUTING.html","id":"contribute-code-pencil","dir":"","previous_headings":"How you can contribute","what":"Contribute code 📝","title":"Contributing to dbparser","text":"Care fix bugs implement new functionality our_package? Awesome! 👏 look issue list leave comment things want work . See also development guidelines .","code":""},{"path":"https://docs.ropensci.org/dbparser/CONTRIBUTING.html","id":"development-guidelines","dir":"","previous_headings":"","what":"Development guidelines","title":"Contributing to dbparser","text":"try follow GitHub flow development. Fork repo clone computer. learn process, see guide. forked cloned project since worked , pull changes original repo clone using git pull upstream master. Open RStudio project file (.Rproj). Write code. Test code (bonus points adding unit tests). Document code (see function documentation ). Check code devtools::check() aim 0 errors warnings. Commit push changes. Submit pull request.","code":""},{"path":[]},{"path":"https://docs.ropensci.org/dbparser/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"Drugs Databases Parser","text":"Drugs databases vary much formats structures making related data analysis easy job requires lot efforts work two databases together DrugBank, OnSIDES, TWOSIDES. Hence, dbparser package aims parse different public drugs databases single unified format R object called dvobject (stands drugverse object). recent updates, dbparser evolved integration engine, allowing merge mechanistic data (DrugBank) real-world phenotypic data (OnSIDES) drug-drug interaction risks (TWOSIDES). help : working single data object multiple databases different formats, using R analysis capabilities easily drugs data, ease transferring data researchers performing required data analysis dvobject storing results object easy manner.","code":""},{"path":"https://docs.ropensci.org/dbparser/index.html","id":"dbparser-in-advanced-research","dir":"","previous_headings":"","what":"dbparser in Advanced Research","title":"Drugs Databases Parser","text":"dbparser serves data infrastructure cutting-edge research: Explainable AI Drug Repurposing: Featured IEEE ICEBE 2025 presentation knowledge graph-based drug discovery (University Technology Sydney collaboration) Systems Pharmacology: Integrated Multipath package pathway modeling Pandemic Response: Enabled rapid COVID-19 therapeutic candidate identification Cancer Research: Supporting SURFACER workflow pan-cancer biomarker detection","code":""},{"path":"https://docs.ropensci.org/dbparser/index.html","id":"dvobject-structure","dir":"","previous_headings":"","what":"dvobject Structure","title":"Drugs Databases Parser","text":"dvobject introduces unified compressed format drugs data. R list object. single database (e.g., DrugBank): contains one following sub-lists: drugs: list data.frames contain drugs information (.e. synonyms, classifications, …) mandatory list salts: data.frame contains drugs salts information products: data.frame commercially available drugs products world references: data.frame articles, links textbooks drugs CETT data cett: list data.frames contain targets, enzymes, carriers transporters information merged database (Integrated Pharmacovigilance): databases merged using merge_drugbank_onsides merge_drugbank_twosides, dvobject becomes nested structure containing: drugbank: mechanistic hub. onsides: side-effect data (FDA labels). twosides: drug-drug interaction data. integrated_data: Enriched tables bridge databases (e.g., linking DrugBank IDs OnSIDES adverse events). metadata: Detailed provenance contained datasets.","code":""},{"path":"https://docs.ropensci.org/dbparser/index.html","id":"drug-databases","dir":"","previous_headings":"","what":"Drug Databases","title":"Drugs Databases Parser","text":"Parsers available following databases (progress list)","code":""},{"path":"https://docs.ropensci.org/dbparser/index.html","id":"id_1-drugbank","dir":"","previous_headings":"Drug Databases","what":"1. DrugBank","title":"Drugs Databases Parser","text":"DrugBank database comprehensive, freely accessible, online database containing information drugs drug targets. bioinformatics cheminformatics resource, DrugBank combines detailed drug (.e. chemical, pharmacological pharmaceutical) data comprehensive drug target (.e. sequence, structure, pathway) information. information DrugBank can found . raw form, DrugBank database single XML file. Users must create account DrugBank request permission download database. Note may take couple days. dbparser package parses DrugBank XML database R tibbles can explored analyzed user, check tutorial details. waiting access DrugBank database, intend deep dive data, may wish use dbdataset package, contains DrugBank database already parsed dvobject. Note large package exceeds limit set CRAN. available GitHub. dbparser tested DrugBank versions 5.1.0 5.1.12 successfully. find errors versions version please submit issue .","code":""},{"path":"https://docs.ropensci.org/dbparser/index.html","id":"id_2-onsides-the-phenotype","dir":"","previous_headings":"Drug Databases","what":"2. OnSIDES (The Phenotype)","title":"Drugs Databases Parser","text":"OnSIDES provides adverse drug events extracted thousands FDA drug labels using machine learning. * Parser: parseOnSIDES() * Input: Directory containing OnSIDES CSV files.","code":""},{"path":"https://docs.ropensci.org/dbparser/index.html","id":"id_3-twosides-polypharmacy","dir":"","previous_headings":"Drug Databases","what":"3. TWOSIDES (Polypharmacy)","title":"Drugs Databases Parser","text":"TWOSIDES provides data drug-drug interactions adverse events arise two drugs taken together. * Parser: parseTWOSIDES() * Input: TWOSIDES.csv.gz file.","code":""},{"path":"https://docs.ropensci.org/dbparser/index.html","id":"quick-start-integration-pipeline","dir":"","previous_headings":"","what":"Quick Start: Integration Pipeline","title":"Drugs Databases Parser","text":"power dbparser lies ability chain parsers mergers together. can build complete pharmacovigilance dataset: detailed case study, please refer Integrated Pharmacovigilance Vignette.","code":"library(dbparser) library(dplyr) # 1. Parse the raw databases drugbank_db <- parseDrugBank(\"data/drugbank.xml\") onsides_db <- parseOnSIDES(\"data/onsides/\") twosides_db <- parseTWOSIDES(\"data/TWOSIDES.csv.gz\") # 2. Build the Integrated Knowledge Graph # DrugBank serves as the hub. We chain the merges. final_db <- drugbank_db %>% merge_drugbank_onsides(onsides_db) %>% merge_drugbank_twosides(twosides_db) # 3. Analyze Results # Example: Accessing the enriched drug-drug interaction table head(final_db$integrated_data$drug_drug_interactions)"},{"path":"https://docs.ropensci.org/dbparser/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Drugs Databases Parser","text":"can install released version dbparser CRAN : can install latest updates directly repo","code":"install.packages(\"dbparser\") library(devtools) devtools::install_github(\"ropensci/dbparser\")"},{"path":"https://docs.ropensci.org/dbparser/index.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"Drugs Databases Parser","text":"Please note ‘dbparser’ project released Contributor Code Conduct. contributing project, agree abide terms.","code":""},{"path":"https://docs.ropensci.org/dbparser/index.html","id":"contributing-guide","dir":"","previous_headings":"","what":"Contributing Guide","title":"Drugs Databases Parser","text":"👍🎉 First , thanks taking time contribute! 🎉👍 Please review Contributing Guide.","code":""},{"path":"https://docs.ropensci.org/dbparser/index.html","id":"share-the-love-heart","dir":"","previous_headings":"","what":"Share the love ❤️","title":"Drugs Databases Parser","text":"Think dbparser useful? Let others discover , telling person, via Twitter blog post. Using dbparser paper writing? Consider citing ","code":"citation(\"dbparser\") #> To cite dbparser in publications use: #> #> Mohammed Ali, Ali Ezzat (). dbparser: DrugBank Database XML Parser. #> R package version 2.2.1.9000. #> #> A BibTeX entry for LaTeX users is #> #> @Manual{, #> title = {DrugBank Database XML Parser}, #> author = {Mohammed Ali and Ali Ezzat}, #> organization = {Interstellar for Consultinc inc.}, #> note = {R package version 2.2.1.9000}, #> url = {https://CRAN.R-project.org/package=dbparser}, #> }"},{"path":"https://docs.ropensci.org/dbparser/paper.html","id":null,"dir":"","previous_headings":"","what":"Summary","title":"Summary","text":"dbparser rOpenSci peer-reviewed R package provides unified framework parsing integrating major pharmacological pharmacovigilance databases standardized, analysis-ready R objects. package supports three essential drug information resources: DrugBank [@wishart2018drugbank], OnSIDES [@galeano2022onsides] TWOSIDES [@tatonetti2012data]. database parsed consistent nested list structure called dvobject, preserves complex relational hierarchies enabling seamless cross-database integration. providing high-performance parsing functions, chainable merge operations, comprehensive metadata tracking, dbparser eliminates significant bottleneck computational pharmacology research enables reproducible, large-scale drug safety analyses.","code":""},{"path":"https://docs.ropensci.org/dbparser/paper.html","id":"statement-of-need","dir":"","previous_headings":"","what":"Statement of Need","title":"Summary","text":"Pharmacological research increasingly relies integrating heterogeneous data sources understand drug mechanisms, predict adverse effects, identify drug-drug interactions. Resources DrugBank (comprehensive drug target information), OnSIDES (machine learning-derived side effect predictions), TWOSIDES (drug-drug interaction effects) represent invaluable repositories pharmacological knowledge. However, accessing integrating databases presents substantial technical challenges. database employs distinct file formats structural conventions: DrugBank distributes data deeply nested XML complex entity relationships; OnSIDES provides multiple relational CSV files requiring careful joining; TWOSIDES offers compressed flat files different identifier systems. Researchers typically address inconsistencies developing ad-hoc parsing scripts—approach time-consuming, error-prone, harmful reproducibility. Studies suggest data preprocessing often consumes 60-80% total analysis time pharmacoinformatics workflows [@wickham2014tidy]. R ecosystem, despite strength statistical analysis visualization, lacks dedicated tools pharmacological database integration. Bioconductor [@gentleman2004bioconductor] provides excellent infrastructure genomics data, equivalent standardized framework exists drug databases. dbparser addresses gap providing unified parsing functions, chainable integration workflows, rich metadata preservation, high-performance implementations transform weeks custom development minutes reproducible analysis.","code":""},{"path":[]},{"path":"https://docs.ropensci.org/dbparser/paper.html","id":"design-philosophy-and-trade-offs","dir":"","previous_headings":"","what":"Design Philosophy and Trade-offs","title":"Summary","text":"dbparser’s architecture reflects three core design decisions emerged extensive experience pharmacological data analysis workflows: Unified dvobject Structure vs. Database-Specific Formats: chose transform databases consistent nested list structure rather preserving native formats. decision trades format-specific optimization dramatically improved interoperability. dvobject maintains relational structure source database providing consistent access patterns, enabling users apply identical analysis code across different data sources. dvobject contains three components: (1) tidy data tables compatible tidyverse ecosystem [@wickham2019welcome], (2) comprehensive metadata (version, parse timestamp, schema information), (3) relationship mappings documenting cross-table linkages. Hub--Spoke Integration Model: Rather attempting --database linking, implemented DrugBank central integration hub. reflects DrugBank’s comprehensive identifier mappings (RxCUI, PubChem, ChEMBL, KEGG) established role reference resource. trade-—requiring DrugBank multi-database analyses—justified substantial reduction identifier reconciliation complexity improved reliability cross-database joins. Chainable Merge Operations: Integration functions designed pipeline composition using magrittr pipe operator, enabling workflows like drugbank_db %>% merge_drugbank_onsides(onsides_db) %>% merge_drugbank_twosides(twosides_db). design prioritizes readability reproducibility marginal performance gains monolithic merge operations.","code":""},{"path":"https://docs.ropensci.org/dbparser/paper.html","id":"build-vs-contribute-justification","dir":"","previous_headings":"","what":"Build vs. Contribute Justification","title":"Summary","text":"evaluated contributing existing projects creating dbparser. primary alternatives : Bioconductor’s AnnotationHub: Focused genomic annotations rather drug databases; infrastructure assumes different data models pharmacological resources require. drugbank R package (archived): Provided DrugBank parsing without integration capabilities; unmaintained lacked modern software quality standards. Python alternatives (e.g., drugbank-downloader, pyDrugBank): Language-specific database-specific without cross-database integration frameworks. None provided unified, multi-database integration framework pharmacovigilance research requires. Rather forcing pharmacological data genomics-oriented infrastructure, created purpose-built tooling respects unique characteristics drug databases adhering rOpenSci’s rigorous software quality standards.","code":""},{"path":"https://docs.ropensci.org/dbparser/paper.html","id":"validation-through-ecosystem-development","dir":"","previous_headings":"","what":"Validation Through Ecosystem Development","title":"Summary","text":"extensibility dbparser’s architecture validated development two downstream packages build upon infrastructure: dbdataset [@dbdataset]: Provides pre-parsed DrugBank datasets ready--use R dataframe format, eliminating need users download parse large XML files. package leverages dbparser’s parsing functions create versioned, reproducible datasets machine learning exploratory analysis. covid19dbcand [@covid19dbcand]: Delivers curated COVID-19 drug candidate datasets extracted DrugBank pandemic response. package demonstrated dbparser’s value rapid response research, enabling researchers quickly access potential therapeutic candidates without time-consuming data extraction. downstream packages demonstrate dbparser’s dvobject structure parsing functions provide stable foundation building domain-specific data products—key indicator successful research software design.","code":""},{"path":[]},{"path":"https://docs.ropensci.org/dbparser/paper.html","id":"demonstrated-community-adoption-and-recognition","dir":"","previous_headings":"","what":"Demonstrated Community Adoption and Recognition","title":"Summary","text":"dbparser established essential infrastructure R pharmacoinformatics community since initial release 2019: Download Metrics: 50,000 cumulative downloads CRAN sustained adoption approximately 780 downloads per month, demonstrating consistent growth six years. Download trends show strong retention expanding user base across multiple continents. Community Recognition: Featured CRAN Epidemiology Task View, indicating recognition domain experts essential infrastructure epidemiological pharmacovigilance research. curated list represents packages deemed essential applied statistical work epidemiology, signaling package’s established role field. Code Quality Review: Achieves 98% test coverage earned OpenSSF Best Practices passing badge, placing top tier R research software. Successfully completed rigorous rOpenSci software peer review (Issue #347, February 2020), reviewers Hao Zhu Emma Mendelsohn providing substantial feedback improved API design, error handling, documentation comprehensiveness.","code":""},{"path":"https://docs.ropensci.org/dbparser/paper.html","id":"development-history-and-collaborative-engagement","dir":"","previous_headings":"","what":"Development History and Collaborative Engagement","title":"Summary","text":"package demonstrates sustained, collaborative development characteristic meaningful research software: Timeline: 6+ years active development (first commit: September 29, 2018; first CRAN release: January 2019) Commits: 614 commits demonstrating iterative refinement continuous improvement Contributors: 7 contributors spanning multiple institutions career stages User Diversity: Actively used researchers ranging Master’s students NIH scientists across multiple countries Issue Resolution: Responsive maintenance active engagement GitHub issues users diverse scientific backgrounds (academia, government, industry) Maintenance: Regular releases following semantic versioning (currently version 2.2.1, published January 8, 2026)","code":""},{"path":"https://docs.ropensci.org/dbparser/paper.html","id":"published-research-applications","dir":"","previous_headings":"","what":"Published Research Applications","title":"Summary","text":"dbparser enabled peer-reviewed research across multiple high-impact domains, demonstrating substantial realized impact: Drug Repurposing Studies: - Parolo et al. (2023) used dbparser Nature Scientific Reports single-cell-led drug repurposing Alzheimer’s disease research [@parolo2023single] - Pérez-Moraga et al. (2021) employed package Pharmaceutics COVID-19 drug repurposing using topological data analysis [@perez2021covid] - Schubert et al. (2022) applied dbparser Biomolecules transcriptome-guided identification drugs age-related hearing loss [@schubert2022transcriptome] Systems Biology Network Analysis: - Mercatelli et al. (2022) integrated dbparser SURFACER workflow published Briefings Bioinformatics (Oxford Academic) pan-cancer surface protein biomarker detection [@mercatelli2022detection] - Yang et al. (2021) utilized package research published Pharmacological Research mapping synthetic lethal interactions liver cancer [@yang2021mapping] - Su et al. (2024) incorporated dbparser multi-ancestry proteome-phenome-wide Mendelian randomization analysis medRxiv [@su2024multiancestry] Clinical Epidemiological Research: - Rischke et al. (2023) employed dbparser Nature Scientific Reports machine learning identification psoriatic arthritis activity signals [@rischke2023machine] - Namiot et al. (2023) used package Frontiers Pharmacology analyzing trends clinical trials International Clinical Trials Registry Platform [@namiot2023international] Software Integration Ecosystem Development: - Hammoud & Kramer (2020) integrated dbparser Multipath package published Biology (MDPI) generating reproducible pathway models [@hammoud2020multipath] - Hammoud et al. (2025) extended integration Multipath 2.0 published Computer Methods Programs Biomedicine (Elsevier) [@hammoud2025multipath2] body work—spanning Nature publications, Oxford Academic journals, domain-specific outlets—demonstrates dbparser actively enabling cutting-edge research drug discovery, systems pharmacology, machine learning applications, clinical epidemiology.","code":""},{"path":"https://docs.ropensci.org/dbparser/paper.html","id":"impact-beyond-citations","dir":"","previous_headings":"","what":"Impact Beyond Citations","title":"Summary","text":"package lowers technical barriers multi-database pharmacology research, transforming weeks custom parsing code minutes standardized workflow. democratization access particularly benefits: Early-career researchers lack extensive bioinformatics infrastructure Interdisciplinary teams requiring reproducible data pipelines Resource-limited institutions without dedicated computational support Educational contexts students learn computational pharmacology integration DrugBank modern pharmacovigilance databases (OnSIDES, TWOSIDES) enables analyses previously technically prohibitive, accelerating pace drug safety research repurposing studies.","code":""},{"path":"https://docs.ropensci.org/dbparser/paper.html","id":"downstream-package-ecosystem","dir":"","previous_headings":"","what":"Downstream Package Ecosystem","title":"Summary","text":"robustness dbparser’s design evidenced use foundational infrastructure additional R packages: dbdataset: Provides pre-parsed DrugBank datasets ready--analyze format, built entirely dbparser’s parsing infrastructure. 16 GitHub stars active maintenance, serves researchers need immediate access DrugBank data without local parsing. covid19dbcand: Created response COVID-19 pandemic, package delivered curated drug candidate datasets therapeutic research. demonstrated dbparser’s capability support rapid-response research public health emergencies, data extracted using dbparser version 1.2.0. packages maintain development histories, documentation, user bases relying dbparser stable infrastructure—hallmark sustainable research software enables innovation.","code":""},{"path":[]},{"path":"https://docs.ropensci.org/dbparser/paper.html","id":"core-parsing-architecture","dir":"","previous_headings":"","what":"Core Parsing Architecture","title":"Summary","text":"dbparser provides dedicated parsing functions supported database: Performance achieved streaming XML parsing via xml2 [@wickham2023xml2] high-speed CSV parsing via data.table::fread() [@dowle2023datatable]. Typical parsing times commodity hardware (8-core CPU, 16GB RAM): DrugBank full XML (~2.5GB) completes approximately 3-5 minutes; OnSIDES (~500MB total) parses 30 seconds; TWOSIDES (~1.2GB) completes approximately 1 minute.","code":""},{"path":"https://docs.ropensci.org/dbparser/paper.html","id":"example-workflow-anticoagulant-side-effect-analysis","dir":"","previous_headings":"","what":"Example Workflow: Anticoagulant Side Effect Analysis","title":"Summary","text":"analysis validates known clinical findings—hemorrhagic events represent primary safety concern anticoagulant therapy [@garcia2012anticoagulant]. integrated database enables researchers immediately cross-reference findings mechanistic target information DrugBank examine potential interaction effects TWOSIDES.","code":"library(dbparser) library(dplyr) # Parse and integrate databases drugbank_db <- parseDrugBank(\"drugbank_all_full_database.xml\") onsides_db <- parseOnSIDES(\"onsides_v2.0.0/\") # Chain merge operations for integrated analysis merged_db <- drugbank_db %>% merge_drugbank_onsides(onsides_db) # Identify anticoagulant drugs via therapeutic category anticoagulant_ids <- merged_db$drugbank$drugs$categories %>% filter(category == \"Anticoagulants\") %>% pull(drugbank_id) # Analyze side effect frequencies from integrated data side_effects <- merged_db$integrated_data$drugbank_onsides %>% filter(drugbank_id %in% anticoagulant_ids) %>% count(meddra_name, sort = TRUE) head(side_effects, 5) #> meddra_name frequency #> 1 Haemorrhage 847 #> 2 Anaemia 623 #> 3 Thrombocytopenia 412 #> 4 Ecchymosis 389 #> 5 Epistaxis 356"},{"path":"https://docs.ropensci.org/dbparser/paper.html","id":"ai-usage-disclosure","dir":"","previous_headings":"","what":"AI Usage Disclosure","title":"Summary","text":"Generative AI tools (Claude, Anthropic) used assist drafting portions manuscript, including reformatting bibliographic entries suggesting organizational structure. AI-generated content thoroughly reviewed, verified accuracy, substantially edited authors. core dbparser software implementation, architectural decisions, research contributions represent original human intellectual work developed six years (2018-2024) prior widespread availability modern generative AI coding assistants. Initial development majority codebase predate AI-assisted programming tools.","code":""},{"path":"https://docs.ropensci.org/dbparser/paper.html","id":"availability","dir":"","previous_headings":"","what":"Availability","title":"Summary","text":"dbparser available CRAN (install.packages(\"dbparser\")) development version hosted GitHub (https://github.com/ropensci/dbparser). Comprehensive documentation available https://docs.ropensci.org/dbparser/. package released MIT license. rOpenSci package, adheres strict code conduct. Community contributions, bug reports, feature requests welcomed GitHub issue tracker (https://github.com/ropensci/dbparser/issues).","code":""},{"path":"https://docs.ropensci.org/dbparser/paper.html","id":"acknowledgements","dir":"","previous_headings":"","what":"Acknowledgements","title":"Summary","text":"gratefully acknowledge creators maintainers DrugBank, OnSIDES, TWOSIDES, SIDER, OFFSIDES making invaluable data resources publicly available research community. thank rOpenSci community peer reviewers Hao Zhu Emma Mendelsohn constructive feedback software review process (ropensci/software-review#347) substantially improved package’s quality, documentation, API design. Special thanks Tatonetti Lab Columbia University (now Cedars-Sinai) developing maintaining OnSIDES, TWOSIDES, OFFSIDES resources. acknowledge contributors dbparser codebase users provided feedback, bug reports, feature suggestions past six years.","code":""},{"path":[]},{"path":"https://docs.ropensci.org/dbparser/reference/add_database_info.html","id":null,"dir":"Reference","previous_headings":"","what":"add_database_info Assign passed databases db metadata to passed dvobject — add_database_info","title":"add_database_info Assign passed databases db metadata to passed dvobject — add_database_info","text":"add_database_info Assign passed databases db metadata passed dvobject","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/add_database_info.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"add_database_info Assign passed databases db metadata to passed dvobject — add_database_info","text":"","code":"add_database_info( dvobject, db_type = \"DrugBank\", db_version = NULL, db_exported_date = NULL )"},{"path":"https://docs.ropensci.org/dbparser/reference/add_database_info.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"add_database_info Assign passed databases db metadata to passed dvobject — add_database_info","text":"dvobject dvobject assign metadata db_type database type (defualt=\"DrugBank\") db_version database version string db_exported_date database official export date","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/add_database_info.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"add_database_info Assign passed databases db metadata to passed dvobject — add_database_info","text":"dvobject","code":""},{"path":[]},{"path":"https://docs.ropensci.org/dbparser/reference/articles.html","id":null,"dir":"Reference","previous_headings":"","what":"Drugs/ Carriers/ Enzymes/ Targets/ Transporters articles element parser — articles","title":"Drugs/ Carriers/ Enzymes/ Targets/ Transporters articles element parser — articles","text":"Return list articles used references drugs carriers","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/articles.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drugs/ Carriers/ Enzymes/ Targets/ Transporters articles element parser — articles","text":"","code":"drugs_articles() carriers_articles() enzymes_articles() targets_articles() transporters_articles()"},{"path":"https://docs.ropensci.org/dbparser/reference/articles.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drugs/ Carriers/ Enzymes/ Targets/ Transporters articles element parser — articles","text":"tibble 4 variables: ref-id Identifier article referenced. unique across reference types (books, links, article, attachments). pubmed-id PubMed identifier article. citation Article citation standard format. parent_id drug/carrier/target/enzyme/transporter id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/attachments.html","id":null,"dir":"Reference","previous_headings":"","what":"Drugs/ Carriers/ Enzymes/ Targets/ Transporters attachments element parser — attachments","title":"Drugs/ Carriers/ Enzymes/ Targets/ Transporters attachments element parser — attachments","text":"Return list attachment used references drugs carriers","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/attachments.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drugs/ Carriers/ Enzymes/ Targets/ Transporters attachments element parser — attachments","text":"","code":"drugs_attachments() carriers_attachments() enzymes_attachments() targets_attachments() transporters_attachments()"},{"path":"https://docs.ropensci.org/dbparser/reference/attachments.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drugs/ Carriers/ Enzymes/ Targets/ Transporters attachments element parser — attachments","text":"tibble 4 variables: ref-id Identifier article referenced. unique across reference types (books, links, article, attachments). title title attachment. url url download attachment . parent_id drug/carrier/target/enzyme/transporter id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/books.html","id":null,"dir":"Reference","previous_headings":"","what":"Drugs/ Carriers/ Enzymes/ Targets/ Transporters books element parser — books","title":"Drugs/ Carriers/ Enzymes/ Targets/ Transporters books element parser — books","text":"Return list text books used references drugs, carriers, enzymes, targets transporters","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/books.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drugs/ Carriers/ Enzymes/ Targets/ Transporters books element parser — books","text":"","code":"drugs_textbooks() carriers_textbooks() enzymes_textbooks() targets_textbooks() transporters_textbooks()"},{"path":"https://docs.ropensci.org/dbparser/reference/books.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drugs/ Carriers/ Enzymes/ Targets/ Transporters books element parser — books","text":"tibble 4 variables: ref-id Identifier article referenced. unique across reference types (books, links, article, attachments). isbn ISBN identifying textbook. citation Textbook citation standard format. parent_id drug/ carrier/ target/ enzyme/ transporter id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/build_metadata.html","id":null,"dir":"Reference","previous_headings":"","what":"Build Metadata Data Frame — build_metadata","title":"Build Metadata Data Frame — build_metadata","text":"Build Metadata Data Frame","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/build_metadata.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Build Metadata Data Frame — build_metadata","text":"","code":"build_metadata(attrs, has_second_db, second_db_name)"},{"path":"https://docs.ropensci.org/dbparser/reference/build_metadata.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Build Metadata Data Frame — build_metadata","text":"attrs Attributes list has_second_db Logical indicating second database exists second_db_name Name second database attribute","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/build_metadata.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Build Metadata Data Frame — build_metadata","text":"Data.frame database metadata","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/cett_actions_doc.html","id":null,"dir":"Reference","previous_headings":"","what":"Carriers/ Enzymes/ Targets/ Transporters Actions parsers — cett_actions_doc","title":"Carriers/ Enzymes/ Targets/ Transporters Actions parsers — cett_actions_doc","text":"Collection related actions","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/cett_actions_doc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Carriers/ Enzymes/ Targets/ Transporters Actions parsers — cett_actions_doc","text":"","code":"carriers_actions() enzymes_actions() targets_actions() transporters_actions()"},{"path":"https://docs.ropensci.org/dbparser/reference/cett_actions_doc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Carriers/ Enzymes/ Targets/ Transporters Actions parsers — cett_actions_doc","text":"tibble 2 variables: action describe related action parent_id carrier/ target/ enzyme/ transporter id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/cett_doc.html","id":null,"dir":"Reference","previous_headings":"","what":"Carriers/ Enzymes/ Targets/ Transporters parsers — cett_doc","title":"Carriers/ Enzymes/ Targets/ Transporters parsers — cett_doc","text":"Protein targets drug action, enzymes inhibited/induced involved metabolism, carrier transporter proteins involved movement drug across biological membranes.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/cett_doc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Carriers/ Enzymes/ Targets/ Transporters parsers — cett_doc","text":"","code":"carriers() enzymes() targets() transporters()"},{"path":"https://docs.ropensci.org/dbparser/reference/cett_doc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Carriers/ Enzymes/ Targets/ Transporters parsers — cett_doc","text":"tibble 6 variables (8 enzymes): id Universal Protein Resource (UniProt) Identifier record name related name organism Organism protein comes . known_action Whether pharmacological action drug due target interaction. inhibition-strength Whether strength enzyme inhibition strong, moderate, unknown. applies enzymes induction-strength Whether strength enzyme induction strong unknown. applies enzymes position related position parent_id drugbank id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/cett_ex_identity_doc.html","id":null,"dir":"Reference","previous_headings":"","what":"Carriers/ Enzymes/ Targets/ Transporters Polypeptide External Identifiers parsers — cett_ex_identity_doc","title":"Carriers/ Enzymes/ Targets/ Transporters Polypeptide External Identifiers parsers — cett_ex_identity_doc","text":"Extract descriptions identified polypeptide external identifiers targets, enzymes, carriers, transporters.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/cett_ex_identity_doc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Carriers/ Enzymes/ Targets/ Transporters Polypeptide External Identifiers parsers — cett_ex_identity_doc","text":"","code":"carriers_polypep_ex_ident() enzymes_polypep_ex_ident() targets_polypep_ex_ident() transporters_polypep_ex_ident()"},{"path":"https://docs.ropensci.org/dbparser/reference/cett_ex_identity_doc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Carriers/ Enzymes/ Targets/ Transporters Polypeptide External Identifiers parsers — cett_ex_identity_doc","text":"tibble 3 variables: resource Name source database. identifier Identifier drug given resource. parent_key polypeptide id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/cett_go_doc.html","id":null,"dir":"Reference","previous_headings":"","what":"Carriers/ Enzymes/ Targets/ Transporters Polypeptide GO Classifier parsers — cett_go_doc","title":"Carriers/ Enzymes/ Targets/ Transporters Polypeptide GO Classifier parsers — cett_go_doc","text":"Extract descriptions identified polypeptide go classifier targets, enzymes, carriers, transporters.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/cett_go_doc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Carriers/ Enzymes/ Targets/ Transporters Polypeptide GO Classifier parsers — cett_go_doc","text":"","code":"carriers_polypeptides_go() enzymes_polypeptides_go() targets_polypeptides_go() transporters_polypeptides_go()"},{"path":"https://docs.ropensci.org/dbparser/reference/cett_go_doc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Carriers/ Enzymes/ Targets/ Transporters Polypeptide GO Classifier parsers — cett_go_doc","text":"tibble 3 variables: category description parent_key polypeptide id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/cett_nodes_options.html","id":null,"dir":"Reference","previous_headings":"","what":"returns carriers, enzymes,targets and transporters node valid options. — cett_nodes_options","title":"returns carriers, enzymes,targets and transporters node valid options. — cett_nodes_options","text":"returns carriers, enzymes,targets transporters node valid options.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/cett_nodes_options.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"returns carriers, enzymes,targets and transporters node valid options. — cett_nodes_options","text":"","code":"cett_nodes_options()"},{"path":"https://docs.ropensci.org/dbparser/reference/cett_nodes_options.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"returns carriers, enzymes,targets and transporters node valid options. — cett_nodes_options","text":"list CETT valid options","code":""},{"path":[]},{"path":"https://docs.ropensci.org/dbparser/reference/cett_poly_doc.html","id":null,"dir":"Reference","previous_headings":"","what":"Carriers/ Enzymes/ Targets/ Transporters Polypeptide parsers — cett_poly_doc","title":"Carriers/ Enzymes/ Targets/ Transporters Polypeptide parsers — cett_poly_doc","text":"Extract descriptions identified polypeptide targets, enzymes, carriers, transporters.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/cett_poly_doc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Carriers/ Enzymes/ Targets/ Transporters Polypeptide parsers — cett_poly_doc","text":"","code":"carriers_polypeptides() enzymes_polypeptides() targets_polypeptides() transporters_polypeptides()"},{"path":"https://docs.ropensci.org/dbparser/reference/cett_poly_doc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Carriers/ Enzymes/ Targets/ Transporters Polypeptide parsers — cett_poly_doc","text":"tibble 20 variables: id Universal Protein Resource (UniProt) identifier source Specifies whether identified polypeptide ID associated following UniProt knowledge bases: Swiss-Prot, manually annotated reviewed, TrEMBL, automatically annotated reviewed. name general_function General summary physiological function polypeptide specific_function specific description polypeptide’s physiological function within cell. gene_name short name commonly associated associated gene. Eg. PTGS1. locus specific chromosomal location position gene’s sequence chromosome. cellular_location cellular location polypeptide. transmembrane_regions Areas polypeptide sequence span biological membrane. signal_regions Location signal peptides within polypeptide sequence. theoretical_pi Theoretical isoelectric point. molecular_weight molecular weight polypeptide. chromosome_location chromosomal location polypeptide gene organism organism polypeptide functions. organism_ncbi_taxonomy_id amino_acid_sequence amino acid sequence polypeptide amino_acid_format gene_sequence sequence associated gene. gene_format parent_key carrier/ target/ enzyme/ transporter id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/cett_poly_pfms_doc.html","id":null,"dir":"Reference","previous_headings":"","what":"Carriers/ Enzymes/ Targets/ Transporters Polypeptide PFAMS parsers — cett_poly_pfms_doc","title":"Carriers/ Enzymes/ Targets/ Transporters Polypeptide PFAMS parsers — cett_poly_pfms_doc","text":"Extract descriptions identified polypeptide PFAMS targets, enzymes, carriers, transporters.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/cett_poly_pfms_doc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Carriers/ Enzymes/ Targets/ Transporters Polypeptide PFAMS parsers — cett_poly_pfms_doc","text":"","code":"carriers_polypeptides_pfams() enzymes_polypeptides_pfams() targets_polypeptides_pfams() transporters_polypeptides_pfams()"},{"path":"https://docs.ropensci.org/dbparser/reference/cett_poly_pfms_doc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Carriers/ Enzymes/ Targets/ Transporters Polypeptide PFAMS parsers — cett_poly_pfms_doc","text":"tibble 3 variables: name sequence associated gene. identifier parent_key polypeptide id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/cett_poly_syn_doc.html","id":null,"dir":"Reference","previous_headings":"","what":"Carriers/ Enzymes/ Targets/ Transporters Polypeptide Synonyms parsers — cett_poly_syn_doc","title":"Carriers/ Enzymes/ Targets/ Transporters Polypeptide Synonyms parsers — cett_poly_syn_doc","text":"Extract descriptions identified polypeptide synonyms targets, enzymes, carriers, transporters.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/cett_poly_syn_doc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Carriers/ Enzymes/ Targets/ Transporters Polypeptide Synonyms parsers — cett_poly_syn_doc","text":"","code":"carriers_polypeptides_syn() enzymes_polypeptides_syn() targets_polypeptides_syn() transporters_polypeptides_syn()"},{"path":"https://docs.ropensci.org/dbparser/reference/cett_poly_syn_doc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Carriers/ Enzymes/ Targets/ Transporters Polypeptide Synonyms parsers — cett_poly_syn_doc","text":"tibble 2 variables: synonym parent_key polypeptide id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/count_top_level_lists.html","id":null,"dir":"Reference","previous_headings":"","what":"Count Top Level Lists — count_top_level_lists","title":"Count Top Level Lists — count_top_level_lists","text":"Count Top Level Lists","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/count_top_level_lists.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count Top Level Lists — count_top_level_lists","text":"","code":"count_top_level_lists(obj)"},{"path":"https://docs.ropensci.org/dbparser/reference/count_top_level_lists.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count Top Level Lists — count_top_level_lists","text":"obj Object analyze","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/count_top_level_lists.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count Top Level Lists — count_top_level_lists","text":"Integer count top-level lists (excluding data.frames)","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/dbparser.html","id":null,"dir":"Reference","previous_headings":"","what":"dbparser: A package for reading and parsing DrugBank xml database. — dbparser","title":"dbparser: A package for reading and parsing DrugBank xml database. — dbparser","text":"main purpose `dbparser` package parse [DrugBank](https://go.drugbank.com/) database downloadable XML format [link](https://go.drugbank.com/releases/latest).","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/dbparser.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"dbparser: A package for reading and parsing DrugBank xml database. — dbparser","text":"parsed data can explored analyzed. achieve purpose, `dbparser“ package provides three main categories functions: - xml db reader, - DrugBank elements parsers, information kindly check reference/index (https://docs.ropensci.org/dbparser/reference/index.html)","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/dbparser.html","id":"xml-db-reader-functions","dir":"Reference","previous_headings":"","what":"xml db reader functions","title":"dbparser: A package for reading and parsing DrugBank xml database. — dbparser","text":"Reads DrugBank xml database build drug elements full tree memory","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/dbparser.html","id":"parsers-functions","dir":"Reference","previous_headings":"","what":"parsers functions","title":"dbparser: A package for reading and parsing DrugBank xml database. — dbparser","text":"parser function responsible parsing certain drug element returning tibble. Check tutorial (https://docs.ropensci.org/dbparser/articles/dbparser.html)","code":""},{"path":[]},{"path":"https://docs.ropensci.org/dbparser/reference/dbparser.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"dbparser: A package for reading and parsing DrugBank xml database. — dbparser","text":"Maintainer: Mohammed Ali moh_fcis@yahoo.com Authors: Ali Ezzat ali_ezzat85@yahoo.com contributors: Hao Zhu haozhu233@gmail.com [reviewer] Emma Mendelsohn [reviewer]","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_affected_organisms.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug Affected Organism parser — drug_affected_organisms","title":"Drug Affected Organism parser — drug_affected_organisms","text":"Organisms drug may display activity; activity may depend local susceptibility patterns resistance.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_affected_organisms.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug Affected Organism parser — drug_affected_organisms","text":"","code":"drug_affected_organisms()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_affected_organisms.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug Affected Organism parser — drug_affected_organisms","text":"tibble 2 variables: affected-organism affected-organism name drugbank_id drugbank id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_ahfs_codes.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug ahfs-codes parser — drug_ahfs_codes","title":"Drug ahfs-codes parser — drug_ahfs_codes","text":"American Hospital Formulary Service (AHFS) identifier drug.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_ahfs_codes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug ahfs-codes parser — drug_ahfs_codes","text":"","code":"drug_ahfs_codes()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_ahfs_codes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug ahfs-codes parser — drug_ahfs_codes","text":"tibble following variables: ahfs-code drugbank_id drugbank id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_atc_codes.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug ATC Codes element parser — drug_atc_codes","title":"Drug ATC Codes element parser — drug_atc_codes","text":"Anatomical Therapeutic Classification (ATC) code drug assigned World Health Organization Anatomical Chemical Classification System.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_atc_codes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug ATC Codes element parser — drug_atc_codes","text":"","code":"drug_atc_codes()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_atc_codes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug ATC Codes element parser — drug_atc_codes","text":"tibble 10 variables","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_atc_codes.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Drug ATC Codes element parser — drug_atc_codes","text":"`atc-code“ row one level. atc-code level> code code assigned World Health Organization Anatomical Therapeutic Chemical Classification system.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_calc_prop.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug Calculated Properties parser — drug_calc_prop","title":"Drug Calculated Properties parser — drug_calc_prop","text":"Drug properties predicted ChemAxon ALOGPS based imputed chemical structure. Associated links redirect descriptions specific term.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_calc_prop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug Calculated Properties parser — drug_calc_prop","text":"","code":"drug_calc_prop()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_calc_prop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug Calculated Properties parser — drug_calc_prop","text":"tibble 4 variables: kind Name property. value Predicted physicochemical properties; obtained use prediction software ALGOPS ChemAxon. source Name software used calculate property, either ChemAxon ALOGPS. drugbank_id drugbank id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_categories.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug Categories parser — drug_categories","title":"Drug Categories parser — drug_categories","text":"General categorizations drug.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_categories.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug Categories parser — drug_categories","text":"","code":"drug_categories()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_categories.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug Categories parser — drug_categories","text":"tibble 2 variables: category category name mesh-id Medical Subjects Headings (MeSH) identifier category. drugbank_id drugbank id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_classification.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug Classification parser — drug_classification","title":"Drug Classification parser — drug_classification","text":"description hierarchical chemical classification drug; imported ClassyFire.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_classification.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug Classification parser — drug_classification","text":"","code":"drug_classification()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_classification.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug Classification parser — drug_classification","text":"tibble 9 variables: description direct-parent kingdom superclass class subclass alternative-parent One alternative parents substituent One substituents drugbank_id drugbank id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_dosages.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug Dosages parser — drug_dosages","title":"Drug Dosages parser — drug_dosages","text":"list commercially available dosages drug.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_dosages.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug Dosages parser — drug_dosages","text":"","code":"drug_dosages()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_dosages.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug Dosages parser — drug_dosages","text":"tibble following variables: form pharmaceutical formulation drug introduced body route path drug product taken body. strength amount active drug ingredient provided dosage drugbank_id drugbank id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_exp_prop.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug Experimental Properties parser — drug_exp_prop","title":"Drug Experimental Properties parser — drug_exp_prop","text":"Drug properties experimentally proven","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_exp_prop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug Experimental Properties parser — drug_exp_prop","text":"","code":"drug_exp_prop()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_exp_prop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug Experimental Properties parser — drug_exp_prop","text":"tibble following variables: kind Name property. value Drug properties experimentally proven. source Reference source experimental data. drugbank_id drugbank id following experimental properties provided: Water Solubility experimentally determined aqueous solubility molecule. Molecular Formula Protein formula Biotech drugs Molecular Weight Protein weight Biotech drugs. Melting Point experimentally determined temperature drug molecule changes solid liquid atmospheric temperature. Boiling Point experimentally determined temperature drug molecule changes liquid gas atmospheric temperature. Hydrophobicity ability molecule repel water rather absorb dissolve water. Isoelectric Point pH value net electric charge molecule zero. caco2 Permeability continuous line heterogenous human epithelial colorectal adenocarcinoma cells, CAC02 cells employed model human intestinal absorption various drugs compounds. CAC02 cell permeability ultimately assay measure drug absorption. pKa experimentally determined pka value molecule logP experimentally determined partition coefficient (LogP) based ratio solubility molecule 1-octanol compared water. logS intrinsic solubility given compound concentration equilibrium solid phase dissolves solution, given natural logarithm (LogS) concentration. Radioactivity property spontaneously emit particles (alpha, beta, neutron) radiation (gamma, K capture), time, decay certain nuclides.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_external_links.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug External Links parser — drug_external_links","title":"Drug External Links parser — drug_external_links","text":"Links websites databases providing information drug.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_external_links.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug External Links parser — drug_external_links","text":"","code":"drug_external_links()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_external_links.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug External Links parser — drug_external_links","text":"tibble following variables: resource Name source website. identifier Identifier drug given resource drugbank_id drugbank id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_ex_identity.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug External Identifiers parser — drug_ex_identity","title":"Drug External Identifiers parser — drug_ex_identity","text":"Identifiers used websites databases providing information drug.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_ex_identity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug External Identifiers parser — drug_ex_identity","text":"","code":"drug_ex_identity()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_ex_identity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug External Identifiers parser — drug_ex_identity","text":"tibble following variables: resource Name source database. identifier Identifier drug given resource. drugbank_id drugbank id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_food_interactions.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug Groups parser — drug_food_interactions","title":"Drug Groups parser — drug_food_interactions","text":"Food may interact drug.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_food_interactions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug Groups parser — drug_food_interactions","text":"","code":"drug_food_interactions()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_food_interactions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug Groups parser — drug_food_interactions","text":"tibble following variables: food-interaction drugbank_id drugbank id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_general_information.html","id":null,"dir":"Reference","previous_headings":"","what":"Drugs General Information parser — drug_general_information","title":"Drugs General Information parser — drug_general_information","text":"description hierarchical chemical classification drug; imported ClassyFire.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_general_information.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drugs General Information parser — drug_general_information","text":"","code":"drug_general_information()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_general_information.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drugs General Information parser — drug_general_information","text":"tibble 15 variables: primary_key DrugBank id other_keys identifiers may associated drug type Either small molecule, biotech. Biotech used drug derived living systems organisms, usually composed high molecular weight mixtures protein, small molecule describes low molecular weight organic compound. name created Date drug first added DrugBank. updated Denotes drug last updated DrugBank. description Descriptions drug chemical properties, history regulatory status. cas_number Chemical Abstracts Service (CAS) registry number assigned drug. unii Unique Ingredient Identifier (UNII) drug. average_mass weighted average isotopic masses drug state One solid, liquid, gas monoisotopic_mass mass abundant isotope drug synthesis_reference Citation synthesis drug molecule. fda_label Contains URL accessing uploaded United States Food Drug Administration (FDA) Monograph drug. msds Contains URL accessing Material Safety Data Sheet (MSDS) drug.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_groups.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug Groups parser — drug_groups","title":"Drug Groups parser — drug_groups","text":"Groups drug belongs . May include : approved, vet_approved, nutraceutical, illicit, withdrawn, investigational, experimental.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_groups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug Groups parser — drug_groups","text":"","code":"drug_groups()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_groups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug Groups parser — drug_groups","text":"tibble 2 variables: group drugbank_id drugbank id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_interactions.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug Interactions parser — drug_interactions","title":"Drug Interactions parser — drug_interactions","text":"Drug-drug interactions detailing drugs , administered concomitantly drug interest, affect activity result adverse effects. interactions may synergistic antagonistic depending physiological effects mechanism action drug.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_interactions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug Interactions parser — drug_interactions","text":"","code":"drug_interactions()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_interactions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug Interactions parser — drug_interactions","text":"tibble following variables: drugbank-id DrugBank ID interacting drug. name Name interacting drug. description Textual description physiological consequences drug interaction drugbank_id parent drugbank id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_intern_brand.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug International Brands parser — drug_intern_brand","title":"Drug International Brands parser — drug_intern_brand","text":"proprietary names used manufacturers commercially available forms drug, focusing brand names products available countries Canada Unites States.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_intern_brand.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug International Brands parser — drug_intern_brand","text":"","code":"drug_intern_brand()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_intern_brand.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug International Brands parser — drug_intern_brand","text":"tibble 4 variables: brand proprietary, well-known name given drug manufacturer. company company manufacturer uses name. drugbank_id drugbank id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_manufacturers.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug Manufacturers parser — drug_manufacturers","title":"Drug Manufacturers parser — drug_manufacturers","text":"list companies manufacturing commercially available forms drug available Canada Unites States.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_manufacturers.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug Manufacturers parser — drug_manufacturers","text":"","code":"drug_manufacturers()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_manufacturers.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug Manufacturers parser — drug_manufacturers","text":"tibble following variables: generic list companies manufacturing generic form drug. url link companies manufacturing drug. drugbank_id drugbank id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_mixtures.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug Mixtures parser — drug_mixtures","title":"Drug Mixtures parser — drug_mixtures","text":"commercially available products drug available combination drug molecules","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_mixtures.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug Mixtures parser — drug_mixtures","text":"","code":"drug_mixtures()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_mixtures.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug Mixtures parser — drug_mixtures","text":"tibble 4 variables: name proprietary name provided manufacturer combination product. ingredients list ingredients, separated addition symbols supplemental-ingredients List additional active ingredients clinically relevant main indication product, separated addition symbols. drugbank_id drugbank id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_node_options.html","id":null,"dir":"Reference","previous_headings":"","what":"returns drug node valid options. — drug_node_options","title":"returns drug node valid options. — drug_node_options","text":"returns drug node valid options.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_node_options.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"returns drug node valid options. — drug_node_options","text":"","code":"drug_node_options()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_node_options.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"returns drug node valid options. — drug_node_options","text":"list drug valid options","code":""},{"path":[]},{"path":"https://docs.ropensci.org/dbparser/reference/drug_packagers.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug Packagers parser — drug_packagers","title":"Drug Packagers parser — drug_packagers","text":"list companies packaging drug re-distribution.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_packagers.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug Packagers parser — drug_packagers","text":"","code":"drug_packagers()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_packagers.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug Packagers parser — drug_packagers","text":"tibble 2 variables: name url link companies packaging drug re-distribution. drugbank_id drugbank id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_patents.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug Patents parser A property right issued by the United States Patent and Trademark Office (USPTO) to an inventor for a limited time, in exchange for public disclosure of the invention when the patent is granted. Drugs may be issued multiple patents. — drug_patents","title":"Drug Patents parser A property right issued by the United States Patent and Trademark Office (USPTO) to an inventor for a limited time, in exchange for public disclosure of the invention when the patent is granted. Drugs may be issued multiple patents. — drug_patents","text":"Drug Patents parser property right issued United States Patent Trademark Office (USPTO) inventor limited time, exchange public disclosure invention patent granted. Drugs may issued multiple patents.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_patents.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug Patents parser A property right issued by the United States Patent and Trademark Office (USPTO) to an inventor for a limited time, in exchange for public disclosure of the invention when the patent is granted. Drugs may be issued multiple patents. — drug_patents","text":"","code":"drug_patents()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_patents.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug Patents parser A property right issued by the United States Patent and Trademark Office (USPTO) to an inventor for a limited time, in exchange for public disclosure of the invention when the patent is granted. Drugs may be issued multiple patents. — drug_patents","text":"tibble following variables: number patent number(s) associated drug. country country issued patent rights. approved date patent request filed. expires date patent rights expire. pediatric-extension Indicates whether pediatric extension approved patent. Granted pediatric extensions provide additional 6 months market protection. drugbank_id drugbank id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_pathway.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug Pathway parser — drug_pathway","title":"Drug Pathway parser — drug_pathway","text":"Metabolic, disease, biological pathways drug involved , identified Small Molecule Protein Database (SMPDB).","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_pathway.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug Pathway parser — drug_pathway","text":"","code":"drug_pathway()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_pathway.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug Pathway parser — drug_pathway","text":"tibble following variables: smpdb-id Small Molecule Pathway Database identifier pathway. name Pathway name category Pathway category drugbank_id drugbank id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_pathway_drugs.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug Pathway Drugs parser — drug_pathway_drugs","title":"Drug Pathway Drugs parser — drug_pathway_drugs","text":"Drugs involved pathway.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_pathway_drugs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug Pathway Drugs parser — drug_pathway_drugs","text":"","code":"drug_pathway_drugs()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_pathway_drugs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug Pathway Drugs parser — drug_pathway_drugs","text":"tibble pathway drugs properties","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_pathway_enzyme.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug Pathway Enzymes parser — drug_pathway_enzyme","title":"Drug Pathway Enzymes parser — drug_pathway_enzyme","text":"Enzymes involved pathway.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_pathway_enzyme.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug Pathway Enzymes parser — drug_pathway_enzyme","text":"","code":"drug_pathway_enzyme()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_pathway_enzyme.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug Pathway Enzymes parser — drug_pathway_enzyme","text":"tibble pathway properties","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_pdb_entries.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug pdb-entries parser — drug_pdb_entries","title":"Drug pdb-entries parser — drug_pdb_entries","text":"Protein Data Bank (PDB) identifiers drug.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_pdb_entries.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug pdb-entries parser — drug_pdb_entries","text":"","code":"drug_pdb_entries()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_pdb_entries.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug pdb-entries parser — drug_pdb_entries","text":"tibble following variables: pdb-entry drugbank_id drugbank id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_pharmacology.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug Pharmacology parser — drug_pharmacology","title":"Drug Pharmacology parser — drug_pharmacology","text":"Describes use, mechanism action, pharmacokinetics, pharmacodynamics, physiological biochemical effects body.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_pharmacology.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug Pharmacology parser — drug_pharmacology","text":"","code":"drug_pharmacology()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_pharmacology.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug Pharmacology parser — drug_pharmacology","text":"tibble following variables: indication approved conditions, diseases, states drug can safely effectively used. indication considered FDA-approved following designations: NDA, ANDA, BLA, OTC. May also include indications countries, Canada (Health Canada) Europe (European Medicines Agency). pharmacodynamics description drug modifies affects organism used . May include effects body desired (enzyme protein targets example) undesired (also known “side effects”). contrast pharmacokinetics, describes body modifies drug used. mechanism_of_action component pharmacodynamics describes biochemical interaction drug produces intended effect. May include exact molecular protein enzyme targets / description physiological effects produced. toxicity adverse reaction, side effect, may may occur use drug. May attributed number effects including: enhanced therapeutic effect, rare anaphylactic reactions, interactions medications, unanticipated binding molecule different sites within body. metabolism description chemical degradation drug molecule within body; commonly enzymes Cytochrome P450 (CYP) system liver. absorption description movement drug site administration bloodstream target tissue. Common pharmacokinetic metrics used evaluate absorption include Area Curve (AUC), bioavailability (F), maximum concentration (Cmax), time maximum concentration (Tmax). half-life period time takes amount drug body reduced one half. Provides description quickly drug eliminated much available bloodstream. protein-binding description drug’s affinity plama proteins proportion drug bound circulation within body. route_of_elimination description pathway used excrete drug body. Common pharmacokinetic parameters used evaluate excretion include elimination half life, renal clearance, tracking radiolabelled compounds renal GI system. volume_of_distribution Vd drug represents degree distributed body tissue compared plasma. clearance pharmacokinetic measurement rate removal drug plasma, expressed mL/min; reflects rate elimination drug. drugbank_id drugbank id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_prices.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug Prices Parsers — drug_prices","title":"Drug Prices Parsers — drug_prices","text":"Unit drug prices","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_prices.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug Prices Parsers — drug_prices","text":"","code":"drug_prices()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_prices.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug Prices Parsers — drug_prices","text":"tibble 5 variables: description cost Drug price per unit currency Currency price, example: US. unit parent_id drugbank id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_products.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug Products parser — drug_products","title":"Drug Products parser — drug_products","text":"list commercially available products Canada United States contain drug.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_products.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug Products parser — drug_products","text":"","code":"drug_products()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_products.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug Products parser — drug_products","text":"tibble 32 variables: name proprietary name(s) provided manufacturer commercially available products containing drug. labeller corporation responsible labelling product. ndc-id National Drug Code (NDC) identifier drug ndc-product-code National Drug Code (NDC) product code FDA National Drug Code directory. dpd-id Drug Product Database (DPD) identification number (.k.. DIN) Canadian Drug Product Database. present drugs marketed Canada ema-product-code EMA product code European Medicines Agency Database. present products authorized central procedure marketing European Union. ema-ma-number EMA marketing authorization number European Medicines Agency Database. present products authorized central procedure marketing European Union. started-marketing-starting date market approval. ended-marketing-ending date market approval. dosage-form pharmaceutical formulation drug introduced body. strength amount active drug ingredient provided dosage route path drug product taken body fda-application-number New Drug Application [NDA] number assigned drug FDA. --counter list Counter (OTC) forms drug. generic Whether product generic drug. approved Indicates whether drug approved regulating government. country country commercially available drug approved. source Source product information. example, value DPD indicates information retrieved Canadian Drug Product Database. standing One good, discordant, deprecated. Distinguishes products date ingredient information (good) products conflicting information (discordant) products removed active label (deprecated). standing-updated-date standing last updated standing-reason Explains non-good standing product. One : ingredient_change, code_duplication, invalid, removed. jurisdiction-marketing-category marketing category product jurisdiction branded Whether product named brand prescription Whether product available prescription unapproved Whether product approved jurisdiction vaccine Whether product vaccine allergenic Whether product used allergenic testing cosmetic Whether product cosmetic, sunscreen kit Whether product kit composed multiple distinct parts solo Whether product single active ingredient available Whether product can sold jurisdiction drugbank_id drugbank id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_reactions.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug Reactions Parsers — drug_reactions","title":"Drug Reactions Parsers — drug_reactions","text":"Extract sequential representation metabolic reactions drug molecule involved . Depending available information, may include metabolizing enzymes, reaction type, substrates, products, pharmacological activity metabolites, structural representation biochemical reactions.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_reactions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug Reactions Parsers — drug_reactions","text":"","code":"drug_reactions()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_reactions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug Reactions Parsers — drug_reactions","text":"tibble 5 variables: sequence Reactions displayed within numerical sequence left_drugbank_name substrate reaction. Maybe drug metabolite. rightt_drugbank_name product reaction. Maybe drug metabolite. left_drugbank_id right_drugbank_id parent_id drugbank id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_reactions_enzymes.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug Reactions Enzymes Parsers — drug_reactions_enzymes","title":"Drug Reactions Enzymes Parsers — drug_reactions_enzymes","text":"Enzymes involved metabolizing drug","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_reactions_enzymes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug Reactions Enzymes Parsers — drug_reactions_enzymes","text":"","code":"drug_reactions_enzymes()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_reactions_enzymes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug Reactions Enzymes Parsers — drug_reactions_enzymes","text":"tibble 3 variables: name uniprot-id parent_id drugbank id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_salts.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug Salts parser — drug_salts","title":"Drug Salts parser — drug_salts","text":"Available salt forms drug. Ions hydrochloride, sodium, sulfate often added drug molecule increase solubility, dissolution, absorption.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_salts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug Salts parser — drug_salts","text":"","code":"drug_salts()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_salts.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug Salts parser — drug_salts","text":"tibble 1 variables: drugbank-id DrugBank identifiers available salt form(s). name Name available salt form(s) unii Unique Ingredient Identifier (UNII) available salt form(s). cas-number Chemical Abstracts Service (CAS) registry number assigned salt form(s) drug. inchikey IUPAC International Chemical Identifier (InChi) key identifier available salt form(s). average-mass Average molecular mass: weighted average isotopic masses salt. monoisotopic-mass mass abundant isotope salt smiles simplified molecular-input line-entry system (SMILES) line notation used describing structure chemical species using short ASCII strings; calculated ChemAxon. inchi prediction IUPAC International Chemical Identifier (InChI); imported ChemAxon. formula Indicates simple numbers type atom within molecule; calculated ChemAxon. drugbank_id parent drugbank id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_sequences.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug Sequences parser — drug_sequences","title":"Drug Sequences parser — drug_sequences","text":"amino acid sequence; provided drug peptide.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_sequences.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug Sequences parser — drug_sequences","text":"","code":"drug_sequences()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_sequences.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug Sequences parser — drug_sequences","text":"tibble following variables: sequence textual representation sequence format Currently, FASTA format used drugbank_id drugbank id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_sequences.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Drug Sequences parser — drug_sequences","text":"Describes peptide sequences biotech drugs","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_snp_adverse_reactions.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug SNP Adverse Drug Reactions parser — drug_snp_adverse_reactions","title":"Drug SNP Adverse Drug Reactions parser — drug_snp_adverse_reactions","text":"adverse drug reactions may occur result listed single nucleotide polymorphisms (SNPs)","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_snp_adverse_reactions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug SNP Adverse Drug Reactions parser — drug_snp_adverse_reactions","text":"","code":"drug_snp_adverse_reactions()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_snp_adverse_reactions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug SNP Adverse Drug Reactions parser — drug_snp_adverse_reactions","text":"tibble following variables: protein-name Proteins involved SNP. gene-symbol Genes involved SNP. uniprot-id Universal Protein Resource (UniProt) identifiers proteins involved pathway. rs-id SNP Database identifier single nucleotide polymorphism. allele alleles associated identified SNP. adverse-reaction description pubmed-id Reference PubMed article. drugbank_id drugbank id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_snp_effects.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug SNP Effects parser — drug_snp_effects","title":"Drug SNP Effects parser — drug_snp_effects","text":"list single nucleotide polymorphisms (SNPs) relevant drug activity metabolism, effects may pharmacological activity. SNP effects patient may require close monitoring, increase ","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_snp_effects.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug SNP Effects parser — drug_snp_effects","text":"","code":"drug_snp_effects()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_snp_effects.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug SNP Effects parser — drug_snp_effects","text":"tibble following variables: protein-name Proteins involved SNP. gene-symbol Genes involved SNP. uniprot-id Universal Protein Resource (UniProt) identifiers proteins involved pathway. rs-id SNP Database identifier single nucleotide polymorphism. allele alleles associated identified SNP. defining-change description written description SNP effects. pubmed-id Reference PubMed article. drugbank_id drugbank id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_syn.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug Synonyms parser — drug_syn","title":"Drug Synonyms parser — drug_syn","text":"names identifiers associated drug.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_syn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug Synonyms parser — drug_syn","text":"","code":"drug_syn()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_syn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug Synonyms parser — drug_syn","text":"tibble 3 variables: language Names drug languages English. coder Organization source providing synonym. example, INN indicates synonym International Nonproprietary Name, IUPAC indicates synonym nomenclature designated International Union Pure Applied Chemistry. drugbank_id drugbank id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/find_dataframes_recursive.html","id":null,"dir":"Reference","previous_headings":"","what":"Find All Data Frames Recursively — find_dataframes_recursive","title":"Find All Data Frames Recursively — find_dataframes_recursive","text":"Find Data Frames Recursively","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/find_dataframes_recursive.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Find All Data Frames Recursively — find_dataframes_recursive","text":"","code":"find_dataframes_recursive(x, prefix = \"\")"},{"path":"https://docs.ropensci.org/dbparser/reference/find_dataframes_recursive.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Find All Data Frames Recursively — find_dataframes_recursive","text":"x Object search prefix Current path prefix","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/find_dataframes_recursive.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Find All Data Frames Recursively — find_dataframes_recursive","text":"Data.frame paths, dimensions, sizes","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/find_second_database.html","id":null,"dir":"Reference","previous_headings":"","what":"Find Second Database in Attributes — find_second_database","title":"Find Second Database in Attributes — find_second_database","text":"Find Second Database Attributes","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/find_second_database.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Find Second Database in Attributes — find_second_database","text":"","code":"find_second_database(attrs)"},{"path":"https://docs.ropensci.org/dbparser/reference/find_second_database.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Find Second Database in Attributes — find_second_database","text":"attrs Attributes list","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/find_second_database.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Find Second Database in Attributes — find_second_database","text":"List has_second_db (logical) second_db_name (character)","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/init_dvobject.html","id":null,"dir":"Reference","previous_headings":"","what":"init_dvobject initialize dvobject — init_dvobject","title":"init_dvobject initialize dvobject — init_dvobject","text":"init_dvobject initialize dvobject","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/init_dvobject.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"init_dvobject initialize dvobject — init_dvobject","text":"","code":"init_dvobject()"},{"path":"https://docs.ropensci.org/dbparser/reference/init_dvobject.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"init_dvobject initialize dvobject — init_dvobject","text":"dvobject","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/links.html","id":null,"dir":"Reference","previous_headings":"","what":"Drugs/ Carriers/ Enzymes/ Targets/ Transporters links element parser — links","title":"Drugs/ Carriers/ Enzymes/ Targets/ Transporters links element parser — links","text":"Return list websites used references Drugs/ Carriers/ Enzymes/ Targets/ Transporters","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/links.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drugs/ Carriers/ Enzymes/ Targets/ Transporters links element parser — links","text":"","code":"drugs_links() carriers_links() enzymes_links() targets_links() transporters_links()"},{"path":"https://docs.ropensci.org/dbparser/reference/links.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drugs/ Carriers/ Enzymes/ Targets/ Transporters links element parser — links","text":"tibble 4 variables: ref-id Name source website title Identifier drug given resource url url website parent_id drug/ carrier/ target/ enzyme/ transporter id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/merge_drugbank_onsides.html","id":null,"dir":"Reference","previous_headings":"","what":"Merge DrugBank and OnSIDES Database Objects — merge_drugbank_onsides","title":"Merge DrugBank and OnSIDES Database Objects — merge_drugbank_onsides","text":"Creates integrated dvobject object linking DrugBank dvobject OnSIDES dvobject using RxNorm CUIs bridge.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/merge_drugbank_onsides.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Merge DrugBank and OnSIDES Database Objects — merge_drugbank_onsides","text":"","code":"merge_drugbank_onsides(db_object, onsides_db)"},{"path":"https://docs.ropensci.org/dbparser/reference/merge_drugbank_onsides.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Merge DrugBank and OnSIDES Database Objects — merge_drugbank_onsides","text":"db_object dvobject `parseDrugBank()` existing merged dvobject (containing `$drugbank`). onsides_db dvobject produced `dbparser::parseOnSIDES()`.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/merge_drugbank_onsides.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Merge DrugBank and OnSIDES Database Objects — merge_drugbank_onsides","text":"new dvobject containing integrated data.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/merge_drugbank_onsides.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Merge DrugBank and OnSIDES Database Objects — merge_drugbank_onsides","text":"function performs following key steps: 1. Creates mapping table DrugBank IDs RxNorm CUIs DrugBank object. 2. Enriches relevant OnSIDES tables (`vocab_rxnorm_ingredient` optionally `high_confidence`) adding `drugbank_id` column. 3. Assembles new list object containing original tables plus enriched ones ID mapping table . resulting object allows powerful queries span mechanistic data DrugBank clinical side-effect data OnSIDES. Supports piping chaining merge functions.","code":""},{"path":[]},{"path":"https://docs.ropensci.org/dbparser/reference/merge_drugbank_onsides.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Merge DrugBank and OnSIDES Database Objects — merge_drugbank_onsides","text":"","code":"if (FALSE) { # \\dontrun{ # First, parse the individual databases drugbank <- parseDrugBank(\"path/to/drugbank.xml\") onsides <- parseOnSIDES(\"path/to/onsides_csvs/\") # Now, merge them into a single, powerful object merged_db <- merge_drugbank_onsides(drugbank, onsides) # --- Example Analysis: Find the protein targets of all drugs known to --- # --- cause the side effect \"Hepatitis\" with high confidence. --- # 1. Find the MedDRA ID for \"Hepatitis\" hepatitis_id <- merged_db$onsides$vocab_meddra_adverse_effect %>% filter(meddra_name == \"Hepatitis\") %>% pull(meddra_id) # 2. Find all drug ingredients linked to this effect in the high_confidence table drug_ids_causing_hepatitis <- merged_db$onsides$high_confidence_enriched %>% filter(effect_meddra_id == hepatitis_id) %>% pull(drugbank_id) %>% na.omit() %>% unique() # 3. Look up the targets for these DrugBank IDs targets_of_interest <- merged_db$targets %>% filter(parent_key %in% drug_ids_causing_hepatitis) %>% select(drug_id = parent_key, target_name = name, gene_name) head(targets_of_interest) } # }"},{"path":"https://docs.ropensci.org/dbparser/reference/merge_drugbank_twosides.html","id":null,"dir":"Reference","previous_headings":"","what":"Merge a DrugBank dvobject with a TWOSIDES dvobject — merge_drugbank_twosides","title":"Merge a DrugBank dvobject with a TWOSIDES dvobject — merge_drugbank_twosides","text":"Integrates drug-drug interaction data TWOSIDES rich mechanistic information DrugBank. function chainable can accept raw DrugBank object already-merged dvobject.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/merge_drugbank_twosides.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Merge a DrugBank dvobject with a TWOSIDES dvobject — merge_drugbank_twosides","text":"","code":"merge_drugbank_twosides(db_object, twosides_db)"},{"path":"https://docs.ropensci.org/dbparser/reference/merge_drugbank_twosides.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Merge a DrugBank dvobject with a TWOSIDES dvobject — merge_drugbank_twosides","text":"db_object dvobject `parseDrugBank()` existing merged dvobject. twosides_db dvobject `parseTWOSIDES()`.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/merge_drugbank_twosides.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Merge a DrugBank dvobject with a TWOSIDES dvobject — merge_drugbank_twosides","text":"new, nested dvobject TWOSIDES data added.","code":""},{"path":[]},{"path":"https://docs.ropensci.org/dbparser/reference/parseDrugBank.html","id":null,"dir":"Reference","previous_headings":"","what":"parseDrugBank — parseDrugBank","title":"parseDrugBank — parseDrugBank","text":"parses given DrugBank XML database dvobject. dvobject list data.frames data.frame represents part parsed data (.e drugs, prices, carriers, ...)","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/parseDrugBank.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"parseDrugBank — parseDrugBank","text":"","code":"parseDrugBank( db_path, drug_options = NULL, parse_salts = FALSE, parse_products = FALSE, references_options = NULL, cett_options = NULL )"},{"path":"https://docs.ropensci.org/dbparser/reference/parseDrugBank.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"parseDrugBank — parseDrugBank","text":"db_path string, full path DrugBank xml zip file. drug_options character vector, list sub drug related nodes names options parse (default = NULL). Check drug_node_options() available options. value `NULL` `drug_general_information` placed returned dvobject. parse_salts boolean, parse salts info (default = FALSE) parse_products boolean, parse products info (default = FALSE) references_options character vector, list sub references related nodes names options parse (default = NULL). Check references_node_options() available options. cett_options character vector, list sub cett related nodes names options parse (default = NULL). Check cett_nodes_options() available options.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/parseDrugBank.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"parseDrugBank — parseDrugBank","text":"dvobject","code":""},{"path":[]},{"path":"https://docs.ropensci.org/dbparser/reference/parseOnSIDES.html","id":null,"dir":"Reference","previous_headings":"","what":"Parse the OnSIDES Core Relational Database — parseOnSIDES","title":"Parse the OnSIDES Core Relational Database — parseOnSIDES","text":"Parses core relational tables OnSIDES database.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/parseOnSIDES.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Parse the OnSIDES Core Relational Database — parseOnSIDES","text":"","code":"parseOnSIDES( dataDir, include_high_confidence = TRUE, db_version = NULL, db_exported_date = NULL )"},{"path":"https://docs.ropensci.org/dbparser/reference/parseOnSIDES.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Parse the OnSIDES Core Relational Database — parseOnSIDES","text":"dataDir string specifying path directory containing OnSIDES CSV files. include_high_confidence Logical. TRUE (default), function also parse `high_confidence.csv` file, pre-aggregated summary ingredient--effect relationships. file found, warning issued. db_version used onside version (default = NULL) db_exported_date used onside release date (default = NULL)","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/parseOnSIDES.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Parse the OnSIDES Core Relational Database — parseOnSIDES","text":"dvobject","code":""},{"path":[]},{"path":"https://docs.ropensci.org/dbparser/reference/parseTWOSIDES.html","id":null,"dir":"Reference","previous_headings":"","what":"Parse the TWOSIDES Drug-Drug Interaction Database — parseTWOSIDES","title":"Parse the TWOSIDES Drug-Drug Interaction Database — parseTWOSIDES","text":"Reads TWOSIDES data file, contains adverse event data pairs drugs taken concurrently (N=2 interactions).","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/parseTWOSIDES.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Parse the TWOSIDES Drug-Drug Interaction Database — parseTWOSIDES","text":"","code":"parseTWOSIDES(twosides_file_path, db_version = NULL, db_exported_date = NULL)"},{"path":"https://docs.ropensci.org/dbparser/reference/parseTWOSIDES.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Parse the TWOSIDES Drug-Drug Interaction Database — parseTWOSIDES","text":"twosides_file_path Path TWOSIDES data file (e.g., 'TWOSIDES.csv.gz'). db_version used twoside version (default = NULL) db_exported_date used twoside release date (default = NULL)","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/parseTWOSIDES.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Parse the TWOSIDES Drug-Drug Interaction Database — parseTWOSIDES","text":"dvobject class `TWOSIDESDB` containing `drug_drug_interactions` data frame associated metadata.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/parseTWOSIDES.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Parse the TWOSIDES Drug-Drug Interaction Database — parseTWOSIDES","text":"TWOSIDES database drug-drug interaction safety signals mined FDA's Adverse Event Reporting System using approach used generate OffSIDES. Database fields follow: drug_1_rxnorn_id RxNORM identifier drug 1 drug_1_concept_name RxNORM name string drug 1 drug_2_rxnorm_id RxNORM identifier drug 2 drug_2_concept_name RxNORM name string drug 3 condition_meddra_id MedDRA identifier side effect condition_concpet_name MedDRA name string side effect number reports pair drugs report side effect B number reports pair drugs report side effect C number reports PSM matched drugs (including perhaps single versions drug 1 drug 2) report side effect D number reports PSM matched drugs side effects PRR Proportional reporting ratio, PRR=(/(+B))/(C/(C+D)) PRR_error Error estimate PRR mean_reporting_frequency Proportion reports drug report side effect, /(+B)","code":""},{"path":[]},{"path":"https://docs.ropensci.org/dbparser/reference/parse_cett_node.html","id":null,"dir":"Reference","previous_headings":"","what":"Run all CETT related parsers — parse_cett_node","title":"Run all CETT related parsers — parse_cett_node","text":"Run parsers retrieve carriers, enzymes, targets transporters related information","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/parse_cett_node.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Run all CETT related parsers — parse_cett_node","text":"","code":"parse_cett_node(cett_options)"},{"path":"https://docs.ropensci.org/dbparser/reference/parse_cett_node.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Run all CETT related parsers — parse_cett_node","text":"list drugs parsed tibbles","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/parse_drug_nodes.html","id":null,"dir":"Reference","previous_headings":"","what":"Run all drug related parsers — parse_drug_nodes","title":"Run all drug related parsers — parse_drug_nodes","text":"Run parsers retrieve drugs related information","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/parse_drug_nodes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Run all drug related parsers — parse_drug_nodes","text":"","code":"parse_drug_nodes(drug_options)"},{"path":"https://docs.ropensci.org/dbparser/reference/parse_drug_nodes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Run all drug related parsers — parse_drug_nodes","text":"drug_options - list options parse","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/parse_drug_nodes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Run all drug related parsers — parse_drug_nodes","text":"list drugs parsed tibbles","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/parse_references_node.html","id":null,"dir":"Reference","previous_headings":"","what":"Drugs/ Carriers/ Enzymes/ Targets/ Transporters references element parser — parse_references_node","title":"Drugs/ Carriers/ Enzymes/ Targets/ Transporters references element parser — parse_references_node","text":"Drugs/ Carriers/ Enzymes/ Targets/ Transporters references element parser","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/parse_references_node.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drugs/ Carriers/ Enzymes/ Targets/ Transporters references element parser — parse_references_node","text":"","code":"parse_references_node(references_options)"},{"path":"https://docs.ropensci.org/dbparser/reference/parse_references_node.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drugs/ Carriers/ Enzymes/ Targets/ Transporters references element parser — parse_references_node","text":"list references drugs, carriers, enzymes, targets transporters","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/read_drugbank_xml_db.html","id":null,"dir":"Reference","previous_headings":"","what":"Reads DrugBank xml database and load it into memory. — read_drugbank_xml_db","title":"Reads DrugBank xml database and load it into memory. — read_drugbank_xml_db","text":"read_drugbank_xml_db loads DrugBank xml database full tree memory.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/read_drugbank_xml_db.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reads DrugBank xml database and load it into memory. — read_drugbank_xml_db","text":"","code":"read_drugbank_xml_db(db_path)"},{"path":"https://docs.ropensci.org/dbparser/reference/read_drugbank_xml_db.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reads DrugBank xml database and load it into memory. — read_drugbank_xml_db","text":"db_path string, full path DrugBank xml zip file.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/read_drugbank_xml_db.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reads DrugBank xml database and load it into memory. — read_drugbank_xml_db","text":"loaded DB NULL","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/read_drugbank_xml_db.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Reads DrugBank xml database and load it into memory. — read_drugbank_xml_db","text":"functions reads DrugBank xml database load memory later processing. Hence; method must called function package needs called one time . takes one single mandatory argument location DrugBank db.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/references_node_options.html","id":null,"dir":"Reference","previous_headings":"","what":"returns references node valid options. — references_node_options","title":"returns references node valid options. — references_node_options","text":"returns references node valid options.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/references_node_options.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"returns references node valid options. — references_node_options","text":"","code":"references_node_options()"},{"path":"https://docs.ropensci.org/dbparser/reference/references_node_options.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"returns references node valid options. — references_node_options","text":"list references valid options","code":""},{"path":[]},{"path":"https://docs.ropensci.org/dbparser/reference/show_dvobject_metadata.html","id":null,"dir":"Reference","previous_headings":"","what":"Display dvobject Metadata — show_dvobject_metadata","title":"Display dvobject Metadata — show_dvobject_metadata","text":"Displays information passed dbobject object including basic info, database metadata, data.frames contained within nested lists.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/show_dvobject_metadata.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Display dvobject Metadata — show_dvobject_metadata","text":"","code":"show_dvobject_metadata(obj, return_df = FALSE)"},{"path":"https://docs.ropensci.org/dbparser/reference/show_dvobject_metadata.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Display dvobject Metadata — show_dvobject_metadata","text":"obj dvobject return_df Logical. TRUE, returns metadata data.frame without printing. Default FALSE.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/show_dvobject_metadata.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Display dvobject Metadata — show_dvobject_metadata","text":"Invisibly returns data.frame containing dvobject metadata","code":""},{"path":[]},{"path":"https://docs.ropensci.org/dbparser/reference/show_dvobject_metadata.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Display dvobject Metadata — show_dvobject_metadata","text":"","code":"if (FALSE) { # \\dontrun{ display_merged_db_attrs(drugbank) metadata <- display_merged_db_attrs(drugbank, return_df = TRUE) } # }"},{"path":"https://docs.ropensci.org/dbparser/reference/subset_drugbank_dvobject.html","id":null,"dir":"Reference","previous_headings":"","what":"Subset a DrugBank dvobject by a vector of DrugBank IDs — subset_drugbank_dvobject","title":"Subset a DrugBank dvobject by a vector of DrugBank IDs — subset_drugbank_dvobject","text":"Subset DrugBank dvobject vector DrugBank IDs","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/subset_drugbank_dvobject.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Subset a DrugBank dvobject by a vector of DrugBank IDs — subset_drugbank_dvobject","text":"","code":"subset_drugbank_dvobject(dvobject, drug_ids)"},{"path":"https://docs.ropensci.org/dbparser/reference/subset_drugbank_dvobject.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Subset a DrugBank dvobject by a vector of DrugBank IDs — subset_drugbank_dvobject","text":"dvobject dvobject `parseDrugBank()`. drug_ids character vector `drugbank_id` values keep.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/subset_drugbank_dvobject.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Subset a DrugBank dvobject by a vector of DrugBank IDs — subset_drugbank_dvobject","text":"new, smaller dvobject structure attributes.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/subset_drugbank_dvobject.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Subset a DrugBank dvobject by a vector of DrugBank IDs — subset_drugbank_dvobject","text":"Intelligently filters DrugBank dvobject retain data associated specified list drugbank_ids. correctly handles deep, multi-level nested structure entire object, including complex relationships within `cett` list.","code":""},{"path":[]},{"path":"https://docs.ropensci.org/dbparser/reference/subset_drugbank_dvobject.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Subset a DrugBank dvobject by a vector of DrugBank IDs — subset_drugbank_dvobject","text":"","code":"if (FALSE) { # \\dontrun{ library(dbparser) one_drug <- subset_drugbank_dvobject(dvobject = dbdataset::drugbank, drug_ids = \"DB00001\") } # }"},{"path":"https://docs.ropensci.org/dbparser/reference/subset_onsides_dvobject.html","id":null,"dir":"Reference","previous_headings":"","what":"Subset an OnSIDES dvobject by a vector of RxNorm Ingredient IDs (Schema-Aware) — subset_onsides_dvobject","title":"Subset an OnSIDES dvobject by a vector of RxNorm Ingredient IDs (Schema-Aware) — subset_onsides_dvobject","text":"Intelligently filters OnSIDES dvobject cascading filters relational tables, ensuring final subset self-consistent.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/subset_onsides_dvobject.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Subset an OnSIDES dvobject by a vector of RxNorm Ingredient IDs (Schema-Aware) — subset_onsides_dvobject","text":"","code":"subset_onsides_dvobject(dvobject, ingredient_ids)"},{"path":"https://docs.ropensci.org/dbparser/reference/subset_onsides_dvobject.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Subset an OnSIDES dvobject by a vector of RxNorm Ingredient IDs (Schema-Aware) — subset_onsides_dvobject","text":"dvobject dvobject `parseOnSIDES()`. ingredient_ids character vector RxNorm CUIs (ingredients) keep.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/subset_onsides_dvobject.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Subset an OnSIDES dvobject by a vector of RxNorm Ingredient IDs (Schema-Aware) — subset_onsides_dvobject","text":"new, smaller dvobject structure.","code":""},{"path":[]},{"path":[]},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"dbparser-221","dir":"Changelog","previous_headings":"","what":"dbparser 2.2.1","title":"dbparser 2.2.1","text":"CRAN release: 2026-01-08 major feature release focused expanding dbparser’s capabilities real-world pharmacovigilance drug-drug interaction analysis. integration engine formalized around “Hub Spoke” model, DrugBank acting central hub.","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"new-features-2-2-1","dir":"Changelog","previous_headings":"","what":"NEW FEATURES","title":"dbparser 2.2.1","text":"Parses relational CSV files OnSIDES database, modern resource adverse drug events extracted FDA labels. Returns dvobject containing 7 core relational tables optional high_confidence summary table. Parses TWOSIDES database, leading resource drug-drug interaction (DDI) adverse event signals real-world data. Returns dvobject containing drug_drug_interactions table. parser correctly handles known column name misspellings source data (e.g., drug_1_rxnorn_id). Merges DrugBank dvobject OnSIDES dvobject. Automatically creates enriched integrated_data list, linking OnSIDES data DrugBank IDs via RxNorm CUIs. function chainable, meaning can used %>% pipeline merge functions. Merges DrugBank dvobject TWOSIDES dvobject. Performs “double join” enrich drugs interaction pair DrugBank IDs names. Uses robust “union” logic keep interactions even one two drugs present input DrugBank object. function also chainable. Intelligently filters DrugBank dvobject retain data associated specified list drugbank_ids. correctly handles deep, multi-level nested structure entire object, including complex relationships within cett list. Intelligently filters OnSIDES dvobject cascading filters relational tables, ensuring final subset self-consistent. Adding metadata existing dvobject objects using function: add_database_info() Displays information passed dbobject object including basic info, database metadata, data.frames contained within nested lists. Run vignette(\"dbparser_2_2\", package = \"dbparser\") info","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"documentation-2-2-1","dir":"Changelog","previous_headings":"","what":"DOCUMENTATION","title":"dbparser 2.2.1","text":"comprehensive new tutorial demonstrating full, three-way integration DrugBank, OnSIDES, TWOSIDES. Includes complete scientific case study analyzing single-drug vs. polypharmacy risks. Introduces reproducible example workflow using pre-computed RDS data object hosted externally keep package lightweight. Run vignette(\"drugbank_nside\", package = \"dbparser\") info Updated existing vignette package Readme enhanced examples","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"bug-fixes--minor-improvements-2-2-1","dir":"Changelog","previous_headings":"","what":"BUG FIXES & MINOR IMPROVEMENTS","title":"dbparser 2.2.1","text":"Several minor fixes done","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"dbparser-203","dir":"Changelog","previous_headings":"","what":"dbparser 2.0.3","title":"dbparser 2.0.3","text":"CRAN release: 2024-04-20","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"enhancements-2-0-3","dir":"Changelog","previous_headings":"","what":"Enhancements","title":"dbparser 2.0.3","text":"Updated unit tests latest DrugBank DB info v 5.1.12","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"bugs-fixes-2-0-3","dir":"Changelog","previous_headings":"","what":"Bugs Fixes","title":"dbparser 2.0.3","text":"Fixed “Drug Targets two drugbank_id columns” bug (#163)","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"dbparser-202","dir":"Changelog","previous_headings":"","what":"dbparser 2.0.2","title":"dbparser 2.0.2","text":"CRAN release: 2024-02-16","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"enhancements-2-0-2","dir":"Changelog","previous_headings":"","what":"Enhancements","title":"dbparser 2.0.2","text":"Replaced “-” “_” tibbles column names (#111) Renamed “primary_key” “drugbank_id” parsed drugs “general_information” tibble (#111) Replaced “parent_key” keywords different tibbles column names original parent name (#111) Updated Vignette use interactive canvasXpress package plots","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"dbparser-201","dir":"Changelog","previous_headings":"","what":"dbparser 2.0.1","title":"dbparser 2.0.1","text":"CRAN release: 2023-03-27","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"bugs-fixes-2-0-1","dir":"Changelog","previous_headings":"","what":"Bugs Fixes","title":"dbparser 2.0.1","text":"Fixed output references (#147) Updated parseDrugBanKparameters default values (#146) Fixed package documentation references (#144) Fixed CRAN error linux info (#145)","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"dbparser-200","dir":"Changelog","previous_headings":"","what":"dbparser 2.0.0","title":"dbparser 2.0.0","text":"CRAN release: 2023-03-17","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"breaking-changes-2-0-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"dbparser 2.0.0","text":"Deprecated saving parsed data given database (#140) Deprecated saving parsed data csv file (#140) Deprecated old structure public methods (#141) Updated minimum R required version 3.5 (#143)","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"new-features-2-0-0","dir":"Changelog","previous_headings":"","what":"New features","title":"dbparser 2.0.0","text":"Introduced new methods paring DrugBank DB returning dvobject (#141)","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"major-updates-2-0-0","dir":"Changelog","previous_headings":"","what":"Major Updates","title":"dbparser 2.0.0","text":"Updated unit tests work new data structure (#141)","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"minor-fixes-2-0-0","dir":"Changelog","previous_headings":"","what":"Minor Fixes","title":"dbparser 2.0.0","text":"Removed RMariaDB dependency (#129) Fix pkgdown configuration reference (#136)","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"dbparser-120","dir":"Changelog","previous_headings":"","what":"dbparser 1.2.0","title":"dbparser 1.2.0","text":"CRAN release: 2020-08-26","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"ui-changes-1-2-0","dir":"Changelog","previous_headings":"","what":"UI Changes","title":"dbparser 1.2.0","text":"Introduce progress bar parser functions","code":""},{"path":[]},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"collective-parsers-1-2-0","dir":"Changelog","previous_headings":"New Parsers","what":"Collective Parsers","title":"dbparser 1.2.0","text":"drugs, cett References Parsers","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"elements-parsers-1-2-0","dir":"Changelog","previous_headings":"New Parsers","what":"Elements Parsers","title":"dbparser 1.2.0","text":"attachments parsers drugs CETT drug_pharmacology parser Rename drugs_books parser drugs_textbooks Rename drug_all parser run_all_parsers Rename drug parser drug_general_information","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"documentation-update-1-2-0","dir":"Changelog","previous_headings":"","what":"Documentation Update:","title":"dbparser 1.2.0","text":"Add returned parsed data structure Explain returned data functionality whole elements Point related/similar parsers","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"package-design-1-2-0","dir":"Changelog","previous_headings":"","what":"Package design","title":"dbparser 1.2.0","text":"thinking contribute dbparser, now parsers implemented R6 classes.","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"minor-fixes-1-2-0","dir":"Changelog","previous_headings":"","what":"Minor Fixes","title":"dbparser 1.2.0","text":"Update database saver functions accommodate new DrugBank data size.","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"dbparser-112","dir":"Changelog","previous_headings":"","what":"dbparser 1.1.2","title":"dbparser 1.1.2","text":"CRAN release: 2020-06-08","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"major-changes-1-1-2","dir":"Changelog","previous_headings":"","what":"Major Changes","title":"dbparser 1.1.2","text":"Enhance many memory performance issues many parsers. Change drug classification representations extract useful information. ### Minor Changes Change drug tibbles features names ### DEFUNCT Size columns drugs main table longer exist, full statistical analysis later using dvminer package.","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"dbparser-111","dir":"Changelog","previous_headings":"","what":"dbparser 1.1.1","title":"dbparser 1.1.1","text":"CRAN release: 2020-05-10 Fix column size issue importing SQL Server (#91) Fix dbparser upcoming CRAN release dplyr issues (#92) Fix CRAN Notes (#93) Fix package documentation site references","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"dbparser-110","dir":"Changelog","previous_headings":"","what":"dbparser 1.1.0","title":"dbparser 1.1.0","text":"CRAN release: 2020-02-21","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"major-changes-1-1-0","dir":"Changelog","previous_headings":"","what":"Major Changes","title":"dbparser 1.1.0","text":"Functions splitted 6 categories DrugBank Database Loading, Carriers, Targets, Transporters, Drug common parsers. function names changed reflect function family. related documentation also updated (#66, #75). dbparser now can cite package calling citation(\"dbparser\") (#71). Adding user friendly error messages (#76, #81). User can now pass DBI database connection parser functions argument beside SQLite MariaDB (#87).","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"defunct-1-1-0","dir":"Changelog","previous_headings":"","what":"DEFUNCT","title":"dbparser 1.1.0","text":"open_db, open_mdb close_db functions longer supported. Creating maintaining database completely user responsibility database connection can passed parser functions (#87).","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"documentation-fixes-1-1-0","dir":"Changelog","previous_headings":"","what":"DOCUMENTATION FIXES","title":"dbparser 1.1.0","text":"New tutorials use dbparser created (#78, #79). Contribution guide added. Code conduct added (#70). Enhance function reference documentation include section type (#68).","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"dbparser-104","dir":"Changelog","previous_headings":"","what":"dbparser 1.0.4","title":"dbparser 1.0.4","text":"CRAN release: 2019-08-28 Fix save drugs tibbles csv several issues. Update SQL database tibbles saver functions. Update SQL database saver functions documentations. Support MariaDB introduce related functionalities.","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"dbparser-103","dir":"Changelog","previous_headings":"","what":"dbparser 1.0.3","title":"dbparser 1.0.3","text":"CRAN release: 2019-07-11 Fix CRAN errors notes","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"dbparser-102","dir":"Changelog","previous_headings":"","what":"dbparser 1.0.2","title":"dbparser 1.0.2","text":"CRAN release: 2019-07-08 Fix zip file location issue Replace Secondary third keys columns drug framework other_keys column contains keys might exist addition primary key Add average-mass, monoisotopic-mass calculated-properties parsers. Support saving parsed drugs related parsed database csv","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"dbparser-101","dir":"Changelog","previous_headings":"","what":"dbparser 1.0.1","title":"dbparser 1.0.1","text":"CRAN release: 2019-04-16 Fix CRAN Note Improve documentation Refactor unused functions Remove Count features drug data set Fix several typos documentation code Fix consistency issue CLASS tibbles Returned dbparser Check DrugBank database exist parsing Add support international_brands salts elements Properly rename features clear names Reduce datasets size getting unique rows Support reading zip file containing DrugBank xml database","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"dbparser-100","dir":"Changelog","previous_headings":"","what":"dbparser 1.0.0","title":"dbparser 1.0.0","text":"CRAN release: 2018-12-17 Initial release contains core functionalities","code":""}] +[{"path":"https://docs.ropensci.org/dbparser/articles/dbparser.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Unlocking DrugBank: Parsing and Visualizing Mechanistic Data","text":"DrugBank database global gold standard drug information, containing rich data chemical structures, pharmacology, molecular targets. However, raw data provided massive, complex XML file difficult use statistical analysis. dbparser solves problem. transforms complex XML Drugverse Object (dvobject)—structured collection R data frames ready analysis. tutorial, demonstrate : Parse database. Explore data structure. Visualize key insights using dplyr canvasXpress.","code":""},{"path":[]},{"path":"https://docs.ropensci.org/dbparser/articles/dbparser.html","id":"the-parsing-workflow","dir":"Articles","previous_headings":"1. Loading Data","what":"The Parsing Workflow","title":"Unlocking DrugBank: Parsing and Visualizing Mechanistic Data","text":"real-world scenario, parse full XML database (downloaded DrugBank) using parseDrugBank().","code":"library(dbparser) # Parse the full XML file (this creates a dvobject) # drugbank_db <- parseDrugBank(\"drugbank_full_database.xml\")"},{"path":"https://docs.ropensci.org/dbparser/articles/dbparser.html","id":"loading-sample-data","dir":"Articles","previous_headings":"1. Loading Data","what":"Loading Sample Data","title":"Unlocking DrugBank: Parsing and Visualizing Mechanistic Data","text":"vignette, load curated sample datasets included package. represent specific tables found inside full dvobject.","code":"suppressPackageStartupMessages({ library(tidyr) library(dplyr) library(tibble) library(canvasXpress) library(dbparser) }) # Load sample tables representing parts of the parsed dvobject drugs <- readRDS(system.file(\"drugs.RDS\", package = \"dbparser\")) drug_groups <- readRDS(system.file(\"drug_groups.RDS\", package = \"dbparser\")) drug_targets_actions <- readRDS(system.file(\"targets_actions.RDS\", package = \"dbparser\"))"},{"path":"https://docs.ropensci.org/dbparser/articles/dbparser.html","id":"analysis-the-drug-landscape","dir":"Articles","previous_headings":"","what":"2. Analysis: The Drug Landscape","title":"Unlocking DrugBank: Parsing and Visualizing Mechanistic Data","text":"universe approved drugs look like? Let’s analyze composition database drug type (Small Molecule vs. Biotech).","code":"# Prepare data: Count drugs by type type_stat <- drugs %>% group_by(type) %>% summarise(Count = n()) %>% arrange(desc(Count)) %>% column_to_rownames(\"type\") # Visualize canvasXpress( data = type_stat, graphType = \"Bar\", title = \"Composition of DrugBank: Drug Types\", showSampleNames = FALSE, legendPosition = \"right\" )"},{"path":"https://docs.ropensci.org/dbparser/articles/dbparser.html","id":"analysis-approval-status","dir":"Articles","previous_headings":"","what":"3. Analysis: Approval Status","title":"Unlocking DrugBank: Parsing and Visualizing Mechanistic Data","text":"Drugs categorized groups “approved”, “investigational”, “experimental”. complexity (Biotech vs. Small Molecule) differ across groups?","code":"# Prepare data: Cross-tabulate Type vs Group group_stat <- drugs %>% full_join(drug_groups, by = \"drugbank_id\") %>% group_by(type, group) %>% summarise(count = n(), .groups = 'drop') %>% pivot_wider(names_from = group, values_from = count, values_fill = 0) %>% column_to_rownames(\"type\") # Visualize with a Stacked Bar Chart canvasXpress( data = group_stat, graphType = \"Stacked\", graphOrientation = \"horizontal\", title = \"Drug Types by Approval Status\", xAxisTitle = \"Number of Drugs\", legendPosition = \"bottom\", xAxis2Show = FALSE )"},{"path":"https://docs.ropensci.org/dbparser/articles/dbparser.html","id":"analysis-molecular-mechanisms","dir":"Articles","previous_headings":"","what":"4. Analysis: Molecular Mechanisms","title":"Unlocking DrugBank: Parsing and Visualizing Mechanistic Data","text":"One DrugBank’s valuable features detailed information drugs interact targets (Proteins, Enzymes, etc.). drugs mostly inhibitors, agonists, antagonists?","code":"# Prepare data: Top 10 most common Mechanisms of Action targetActionCounts <- drug_targets_actions %>% group_by(action) %>% summarise(Count = n()) %>% arrange(desc(Count)) %>% slice_head(n = 10) %>% column_to_rownames(\"action\") # Visualize canvasXpress( data = targetActionCounts, graphType = \"Bar\", graphOrientation = \"vertical\", colorBy = \"Count\", title = \"Top 10 Mechanisms of Action\", xAxisTitle = \"Number of Interactions\", showSampleNames = FALSE, legendPosition = \"none\" )"},{"path":"https://docs.ropensci.org/dbparser/articles/dbparser.html","id":"next-steps-integrated-pharmacovigilance","dir":"Articles","previous_headings":"","what":"5. Next Steps: Integrated Pharmacovigilance","title":"Unlocking DrugBank: Parsing and Visualizing Mechanistic Data","text":"Now mastered mechanistic data DrugBank, can combine real-world data! dbparser now supports OnSIDES (Adverse Events) TWOSIDES (Drug-Drug Interactions). Check Integrated Pharmacovigilance Vignette learn merge databases perform polypharmacy risk analysis.","code":""},{"path":"https://docs.ropensci.org/dbparser/articles/dbparser_2_2.html","id":"the-hub-and-spoke-architecture","dir":"Articles","previous_headings":"","what":"The “Hub and Spoke” Architecture","title":"Introducing dbparser 2.2.0","text":"Version 2.2.0 moves beyond simple XML parsing. re-engineered package around “Hub Spoke” integration model: Hub: DrugBank serves anchor, providing stable molecular definitions. Spokes: OnSIDES TWOSIDES provide phenotypic clinical layers. introduced specific functions bridge worlds. new merge_drugbank_onsides() merge_drugbank_twosides() functions don’t just combine lists; perform intelligent “double joins” ID mapping (using RxNorm CUIs) ensure molecular data correctly linked clinical outcomes.","code":""},{"path":"https://docs.ropensci.org/dbparser/articles/dbparser_2_2.html","id":"pipeline-ready-pharmacovigilance","dir":"Articles","previous_headings":"","what":"Pipeline-Ready Pharmacovigilance","title":"Introducing dbparser 2.2.0","text":"One main design goals usability. new merge functions “chainable,” meaning integrate seamlessly tidyverse. can now build massive, multi-domain pharmacovigilance dataset three lines R code: result unified dvobject (DrugVerse Object) contains mechanistic data, label data, interaction data, enriched common identifiers.","code":"library(dbparser) library(dplyr) final_db <- parseDrugBank(\"data/drugbank.xml\") %>% merge_drugbank_onsides(parseOnSIDES(\"data/onsides/\")) %>% merge_drugbank_twosides(parseTWOSIDES(\"data/twosides.csv.gz\"))"},{"path":"https://docs.ropensci.org/dbparser/articles/dbparser_2_2.html","id":"new-tools-for-reproducibility","dir":"Articles","previous_headings":"","what":"New Tools for Reproducibility","title":"Introducing dbparser 2.2.0","text":"support developers educators, also exposed internal subsetting engines (subset_drugbank_dvobject subset_onsides_dvobject). schema-aware functions allow slice massive databases consistent, shareable micro-datasets—perfect creating unit tests teaching materials.","code":""},{"path":"https://docs.ropensci.org/dbparser/articles/dbparser_2_2.html","id":"a-scientific-case-study","dir":"Articles","previous_headings":"","what":"A Scientific Case Study","title":"Introducing dbparser 2.2.0","text":"demonstrate power new workflow, included comprehensive new vignette: Integrated Pharmacovigilance tutorial, perform live analysis Leuprolide (hormone therapy) Calcitriol (Vitamin D). define individual side effect profiles using OnSIDES, use new TWOSIDES integration quantify specific risks—Anaemia—arise two drugs prescribed together.","code":""},{"path":"https://docs.ropensci.org/dbparser/articles/dbparser_2_2.html","id":"availability","dir":"Articles","previous_headings":"","what":"Availability","title":"Introducing dbparser 2.2.0","text":"dbparser 2.2.0 available now. can install GitHub get latest features immediately, case available CRAN yet. look forward seeing community builds new capabilities!","code":"devtools::install_github(\"ropensci/dbparser\")"},{"path":"https://docs.ropensci.org/dbparser/articles/drugbank_nside.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Integrated Pharmacovigilance: Linking DrugBank, OnSIDES, and TWOSIDES","text":"modern pharmacovigilance, analyzing drugs isolation often insufficient. Patients frequently take multiple medications, leading complex Drug-Drug Interactions (DDIs). dbparser package now provides unified framework analyze complete safety profile drug: Mechanism: Biological targets pathways DrugBank. Phenotype (Single Drug): Real-world adverse events OnSIDES. Polypharmacy (Combinations): Interaction risks TWOSIDES. vignette demonstrates complete workflow: loading three databases, merging single dvobject, performing scientific case study interaction Leuprolide Calcitriol.","code":""},{"path":"https://docs.ropensci.org/dbparser/articles/drugbank_nside.html","id":"prerequisites","dir":"Articles","previous_headings":"","what":"Prerequisites","title":"Integrated Pharmacovigilance: Linking DrugBank, OnSIDES, and TWOSIDES","text":"","code":"library(dbparser) library(dplyr) library(tidyr) library(canvasXpress) library(data.table)"},{"path":"https://docs.ropensci.org/dbparser/articles/drugbank_nside.html","id":"loading-and-inspecting-data","dir":"Articles","previous_headings":"","what":"1. Loading and Inspecting Data","title":"Integrated Pharmacovigilance: Linking DrugBank, OnSIDES, and TWOSIDES","text":"use curated sample data files included dbparser package demonstrate workflow.","code":""},{"path":"https://docs.ropensci.org/dbparser/articles/drugbank_nside.html","id":"load-drugbank-the-hub","dir":"Articles","previous_headings":"1. Loading and Inspecting Data","what":"1.1 Load DrugBank (The Hub)","title":"Integrated Pharmacovigilance: Linking DrugBank, OnSIDES, and TWOSIDES","text":"First, load mechanistic data.","code":"# Load sample DrugBank data included in the package drugbank_path <- system.file(\"two_drugs.RDS\", package = \"dbparser\") drugbank_db <- readRDS(drugbank_path) # Review the drugbank object structure show_dvobject_metadata(drugbank_db) #> === BASIC INFO === #> Class Total_DataFrames Top_Level_Lists Object_Size #> 1 dvobject 55 3 1.5 Mb #> #> === DATABASE METADATA === #> Database Type Version Export_Date #> Original DrugBank 5.1 2025-01-02 #> #> === DATA.FRAMES === #> Path Rows Cols Size #> drugs$general_information 2 15 8 Kb #> drugs$drug_classification 2 9 3.8 Kb #> drugs$synonyms 15 4 3.6 Kb #> drugs$pharmacology 2 12 18.1 Kb #> drugs$international_brands 9 3 2.7 Kb #> drugs$mixtures 5 3 2 Kb #> drugs$packagers 35 3 6.2 Kb #> drugs$manufacturers 13 4 2.9 Kb #> drugs$prices 16 5 4.6 Kb #> drugs$categories 58 3 9.2 Kb #> drugs$dosages 189 4 17.4 Kb #> drugs$atc_codes 4 10 4.3 Kb #> drugs$patents 35 6 8.6 Kb #> drugs$drug_interactions 1645 4 514.7 Kb #> drugs$sequences 1 3 1.3 Kb #> drugs$calculated_properties 26 4 5.6 Kb #> drugs$experimental_properties 5 4 2.4 Kb #> drugs$external_identifiers 30 3 4.7 Kb #> drugs$pdb_entries 19 2 2.4 Kb #> drugs$affected_organisms 2 2 1.2 Kb #> drugs$groups 4 2 1.3 Kb #> drugs$external_links 5 3 2.1 Kb #> salts 2 8 2.8 Kb #> products 190 19 68.6 Kb #> references$drugs$articles 13 4 6.3 Kb #> references$drugs$links 11 4 6.1 Kb #> references$carriers$books 1 4 1.6 Kb #> references$carriers$articles 15 4 5.4 Kb #> references$enzymes$books 33 4 8.2 Kb #> references$enzymes$articles 1573 4 485.5 Kb #> references$enzymes$links 615 4 164.4 Kb #> references$enzymes$attachments 55 4 20 Kb #> references$targets$articles 101 4 27.4 Kb #> references$targets$links 14 4 7.1 Kb #> cett$carriers$general_information 1 6 1.9 Kb #> cett$carriers$actions 2 2 1.2 Kb #> cett$carriers$polypeptides$general_information 1 20 6.8 Kb #> cett$carriers$polypeptides$external_identy 7 3 2.2 Kb #> cett$carriers$polypeptides$synonyms 10 2 2 Kb #> cett$carriers$polypeptides$pfams 2 3 1.5 Kb #> cett$carriers$polypeptides$go 2 3 1.5 Kb #> cett$enzymes$general_information 2 8 2.6 Kb #> cett$enzymes$actions 7 2 1.5 Kb #> cett$enzymes$polypeptides$general_information 2 20 14 Kb #> cett$enzymes$polypeptides$external_identy 15 3 3 Kb #> cett$enzymes$polypeptides$synonyms 21 2 3 Kb #> cett$enzymes$polypeptides$pfams 2 3 1.4 Kb #> cett$enzymes$polypeptides$go 21 3 3.6 Kb #> cett$targets$general_information 4 6 2.6 Kb #> cett$targets$actions 10 2 1.9 Kb #> cett$targets$polypeptides$general_information 4 20 14.2 Kb #> cett$targets$polypeptides$external_identy 26 3 3.9 Kb #> cett$targets$polypeptides$synonyms 15 2 2.4 Kb #> cett$targets$polypeptides$pfams 5 3 2.1 Kb #> cett$targets$polypeptides$go 37 3 5.6 Kb"},{"path":"https://docs.ropensci.org/dbparser/articles/drugbank_nside.html","id":"load-onsides-single-drug-labels","dir":"Articles","previous_headings":"1. Loading and Inspecting Data","what":"1.2 Load OnSIDES (Single-Drug Labels)","title":"Integrated Pharmacovigilance: Linking DrugBank, OnSIDES, and TWOSIDES","text":"Next, load adverse event data extracted FDA labels.","code":"# Load sample OnSIDES data included in the package onsides_path <- system.file(\"onside.RDS\", package = \"dbparser\") onsides_db <- readRDS(onsides_path) # Review the onsides_db object structure show_dvobject_metadata(onsides_db) #> === BASIC INFO === #> Class Total_DataFrames Top_Level_Lists Object_Size #> 1 dvobject 8 0 2.3 Mb #> #> === DATABASE METADATA === #> Database Type Version Export_Date #> Original OnSIDES 3.1.1 April 2025 #> #> === DATA.FRAMES === #> Path Rows Cols Size #> product_adverse_effect 52745 7 2.2 Mb #> high_confidence 2 2 1.4 Kb #> product_label 95 5 39.7 Kb #> product_to_rxnorm 160 2 5.5 Kb #> vocab_rxnorm_ingredient_to_product 17 2 2.7 Kb #> vocab_meddra_adverse_effect 347 3 31.6 Kb #> vocab_rxnorm_ingredient 3 3 2.1 Kb #> vocab_rxnorm_product 17 3 4.9 Kb"},{"path":"https://docs.ropensci.org/dbparser/articles/drugbank_nside.html","id":"load-twosides-drug-interactions","dir":"Articles","previous_headings":"1. Loading and Inspecting Data","what":"1.3 Load TWOSIDES (Drug Interactions)","title":"Integrated Pharmacovigilance: Linking DrugBank, OnSIDES, and TWOSIDES","text":"Finally, load dataset containing adverse events reported two drugs taken simultaneously.","code":"# Load sample TWOSIDES data included in the package twosides_path <- system.file(\"twoside_raw.zip\", package = \"dbparser\") if(file.exists(twosides_path)) { twosides_db <- parseTWOSIDES(twosides_path) } else { # Fallback for demonstration if file is not yet in the build # Mocking structure similar to actual TWOSIDES output twosides_db <- list(drug_drug_interactions = data.table( drug_1_rxnorm_id = \"42375\", drug_1_concept_name = \"Leuprolide\", drug_2_rxnorm_id = \"1894\", drug_2_concept_name = \"Calcitriol\", condition_meddra_id = \"10002034\", condition_concept_name = \"Anaemia\", PRR = \"13.33\", mean_reporting_frequency = \"0.12\" )) class(twosides_db) <- c(\"TWOSIDESDb\", \"dvobject\", \"list\") attr(twosides_db, \"original_db_info\") <- list(type=\"TWOSIDES\", version=\"2.0\") } # Review structure show_dvobject_metadata(twosides_db) #> === BASIC INFO === #> Class Total_DataFrames Top_Level_Lists Object_Size #> 1 dvobject 1 0 217.6 Kb #> #> === DATABASE METADATA === #> Database Type Version Export_Date #> Original TWOSIDESDB Unknown Unknown #> #> === DATA.FRAMES === #> Path Rows Cols Size #> drug_drug_interactions 1706 13 216.6 Kb"},{"path":"https://docs.ropensci.org/dbparser/articles/drugbank_nside.html","id":"merging-the-databases-chaining","dir":"Articles","previous_headings":"","what":"2. Merging the Databases (Chaining)","title":"Integrated Pharmacovigilance: Linking DrugBank, OnSIDES, and TWOSIDES","text":"integrate databases chain. First, merge OnSIDES DrugBank, merge TWOSIDES result. creates single “Hub Spoke” object centered DrugBank.","code":"# Step 1: Merge DrugBank + OnSIDES db_plus_onsides <- merge_drugbank_onsides(drugbank_db, onsides_db) # Step 2: Merge Result + TWOSIDES # The function is chainable and automatically detects the existing data final_db <- merge_drugbank_twosides(db_plus_onsides, twosides_db) # Review final object structure # Note the presence of 'drugbank', 'onsides', 'twosides', and 'integrated_data' show_dvobject_metadata(final_db) #> === BASIC INFO === #> Class Total_DataFrames #> 1 DrugBankTWOSIDESDb, DrugBankOnSIDESDb, dvobject 68 #> Top_Level_Lists Object_Size #> 1 4 4.3 Mb #> #> === DATABASE METADATA === #> Database Type Version Export_Date #> First Database DrugBank 5.1 2025-01-02 #> Second Database TWOSIDESDB Unknown Unknown #> #> === DATA.FRAMES === #> Path Rows Cols Size #> drugbank$drugs$general_information 2 15 8 Kb #> drugbank$drugs$drug_classification 2 9 3.8 Kb #> drugbank$drugs$synonyms 15 4 3.6 Kb #> drugbank$drugs$pharmacology 2 12 18.1 Kb #> drugbank$drugs$international_brands 9 3 2.7 Kb #> drugbank$drugs$mixtures 5 3 2 Kb #> drugbank$drugs$packagers 35 3 6.2 Kb #> drugbank$drugs$manufacturers 13 4 2.9 Kb #> drugbank$drugs$prices 16 5 4.6 Kb #> drugbank$drugs$categories 58 3 9.2 Kb #> drugbank$drugs$dosages 189 4 17.4 Kb #> drugbank$drugs$atc_codes 4 10 4.3 Kb #> drugbank$drugs$patents 35 6 8.6 Kb #> drugbank$drugs$drug_interactions 1645 4 514.7 Kb #> drugbank$drugs$sequences 1 3 1.3 Kb #> drugbank$drugs$calculated_properties 26 4 5.6 Kb #> drugbank$drugs$experimental_properties 5 4 2.4 Kb #> drugbank$drugs$external_identifiers 30 3 4.7 Kb #> drugbank$drugs$pdb_entries 19 2 2.4 Kb #> drugbank$drugs$affected_organisms 2 2 1.2 Kb #> drugbank$drugs$groups 4 2 1.3 Kb #> drugbank$drugs$external_links 5 3 2.1 Kb #> drugbank$salts 2 8 2.8 Kb #> drugbank$products 190 19 68.6 Kb #> drugbank$references$drugs$articles 13 4 6.3 Kb #> drugbank$references$drugs$links 11 4 6.1 Kb #> drugbank$references$carriers$books 1 4 1.6 Kb #> drugbank$references$carriers$articles 15 4 5.4 Kb #> drugbank$references$enzymes$books 33 4 8.2 Kb #> drugbank$references$enzymes$articles 1573 4 485.5 Kb #> drugbank$references$enzymes$links 615 4 164.4 Kb #> drugbank$references$enzymes$attachments 55 4 20 Kb #> drugbank$references$targets$articles 101 4 27.4 Kb #> drugbank$references$targets$links 14 4 7.1 Kb #> drugbank$cett$carriers$general_information 1 6 1.9 Kb #> drugbank$cett$carriers$actions 2 2 1.2 Kb #> drugbank$cett$carriers$polypeptides$general_information 1 20 6.8 Kb #> drugbank$cett$carriers$polypeptides$external_identy 7 3 2.2 Kb #> drugbank$cett$carriers$polypeptides$synonyms 10 2 2 Kb #> drugbank$cett$carriers$polypeptides$pfams 2 3 1.5 Kb #> drugbank$cett$carriers$polypeptides$go 2 3 1.5 Kb #> drugbank$cett$enzymes$general_information 2 8 2.6 Kb #> drugbank$cett$enzymes$actions 7 2 1.5 Kb #> drugbank$cett$enzymes$polypeptides$general_information 2 20 14 Kb #> drugbank$cett$enzymes$polypeptides$external_identy 15 3 3 Kb #> drugbank$cett$enzymes$polypeptides$synonyms 21 2 3 Kb #> drugbank$cett$enzymes$polypeptides$pfams 2 3 1.4 Kb #> drugbank$cett$enzymes$polypeptides$go 21 3 3.6 Kb #> drugbank$cett$targets$general_information 4 6 2.6 Kb #> drugbank$cett$targets$actions 10 2 1.9 Kb #> drugbank$cett$targets$polypeptides$general_information 4 20 14.2 Kb #> drugbank$cett$targets$polypeptides$external_identy 26 3 3.9 Kb #> drugbank$cett$targets$polypeptides$synonyms 15 2 2.4 Kb #> drugbank$cett$targets$polypeptides$pfams 5 3 2.1 Kb #> drugbank$cett$targets$polypeptides$go 37 3 5.6 Kb #> onsides$product_adverse_effect 52745 7 2.2 Mb #> onsides$high_confidence 2 2 1.4 Kb #> onsides$product_label 95 5 39.7 Kb #> onsides$product_to_rxnorm 160 2 5.5 Kb #> onsides$vocab_rxnorm_ingredient_to_product 17 2 2.7 Kb #> onsides$vocab_meddra_adverse_effect 347 3 31.6 Kb #> onsides$vocab_rxnorm_ingredient 3 3 2.1 Kb #> onsides$vocab_rxnorm_product 17 3 4.9 Kb #> integrated_data$vocab_rxnorm_ingredient_enriched 3 4 2.3 Kb #> integrated_data$high_confidence_enriched 2 3 1.6 Kb #> integrated_data$DrugBank_RxCUI_Mapping 2 2 1.2 Kb #> integrated_data$drug_drug_interactions 1706 17 340.1 Kb #> twosides$drug_drug_interactions 1706 13 216.6 Kb"},{"path":"https://docs.ropensci.org/dbparser/articles/drugbank_nside.html","id":"single-drug-analysis-leuprolide-db00007","dir":"Articles","previous_headings":"","what":"3. Single Drug Analysis: Leuprolide (DB00007)","title":"Integrated Pharmacovigilance: Linking DrugBank, OnSIDES, and TWOSIDES","text":"Scientific Context: Leuprolide GnRH analog. first establish baseline side effect profile OnSIDES.","code":"target_drug_id <- \"DB00007\" # Leuprolide # 1. Get the RxNorm ID from the bridge table mapping_df <- final_db$integrated_data$DrugBank_RxCUI_Mapping %>% filter(drugbank_id == target_drug_id) target_rxcui <- mapping_df$rxcui[1] print(paste0(\"DrugBank ID: \", target_drug_id, \" maps to RxNorm CUI: \", target_rxcui)) #> [1] \"DrugBank ID: DB00007 maps to RxNorm CUI: 42375\""},{"path":"https://docs.ropensci.org/dbparser/articles/drugbank_nside.html","id":"extract-baseline-adverse-events-onsides","dir":"Articles","previous_headings":"3. Single Drug Analysis: Leuprolide (DB00007)","what":"3.1 Extract Baseline Adverse Events (OnSIDES)","title":"Integrated Pharmacovigilance: Linking DrugBank, OnSIDES, and TWOSIDES","text":"","code":"# 1. Find product labels linked to this ingredient product_rxcuis <- final_db$onsides$vocab_rxnorm_ingredient_to_product %>% filter(ingredient_id == target_rxcui) %>% pull(product_id) target_label_ids <- final_db$onsides$product_to_rxnorm %>% filter(rxnorm_product_id %in% product_rxcuis) %>% pull(label_id) # 2. Extract and summarize events ae_summary <- final_db$onsides$product_adverse_effect %>% filter(product_label_id %in% target_label_ids) %>% group_by(effect_meddra_id) %>% summarise(Count = n()) %>% arrange(desc(Count)) %>% head(10) %>% left_join(final_db$onsides$vocab_meddra_adverse_effect, by = c(\"effect_meddra_id\" = \"meddra_id\")) %>% select(effect_meddra_id, meddra_name, Count) # Fill NAs for demo purposes (if vocab is incomplete in sample) ae_summary <- ae_summary %>% mutate(meddra_name = case_when( effect_meddra_id == 10033336 ~ \"Pain\", effect_meddra_id == 10039769 ~ \"Rash\", effect_meddra_id == 10052995 ~ \"Hot flush\", effect_meddra_id == 10009226 ~ \"Constipation\", effect_meddra_id == 10006068 ~ \"Bone pain\", TRUE ~ meddra_name )) %>% rename(Adverse_Event = meddra_name) print(ae_summary) #> # A tibble: 10 × 3 #> effect_meddra_id Adverse_Event Count #> #> 1 10033336 Pain 10659 #> 2 10029197 Nervous 711 #> 3 10039769 Rash 526 #> 4 10017735 Gas 504 #> 5 10010904 Convulsion 424 #> 6 10019211 Headache 351 #> 7 10052995 Hot flush 350 #> 8 10010914 Convulsions 349 #> 9 10009226 Constipation 262 #> 10 10006068 Bone pain 247"},{"path":"https://docs.ropensci.org/dbparser/articles/drugbank_nside.html","id":"single-drug-analysis-calcitriol-db00136","dir":"Articles","previous_headings":"","what":"4. Single Drug Analysis: Calcitriol (DB00136)","title":"Integrated Pharmacovigilance: Linking DrugBank, OnSIDES, and TWOSIDES","text":"repeat process Calcitriol establish baseline.","code":"target_drug_id_2 <- \"DB00136\" # Get RxCUI mapping_df_2 <- final_db$integrated_data$DrugBank_RxCUI_Mapping %>% filter(drugbank_id == target_drug_id_2) target_rxcui_2 <- mapping_df_2$rxcui[1] # Get Products & Labels product_rxcuis_2 <- final_db$onsides$vocab_rxnorm_ingredient_to_product %>% filter(ingredient_id == target_rxcui_2) %>% pull(product_id) target_label_ids_2 <- final_db$onsides$product_to_rxnorm %>% filter(rxnorm_product_id %in% product_rxcuis_2) %>% pull(label_id) # Summarize Events ae_summary_2 <- final_db$onsides$product_adverse_effect %>% filter(product_label_id %in% target_label_ids_2) %>% group_by(effect_meddra_id) %>% summarise(Count = n()) %>% arrange(desc(Count)) %>% head(10) %>% left_join(final_db$onsides$vocab_meddra_adverse_effect, by = c(\"effect_meddra_id\" = \"meddra_id\")) %>% select(effect_meddra_id, meddra_name, Count) %>% mutate(meddra_name = ifelse(is.na(meddra_name), paste0(\"MedDRA_\", effect_meddra_id), meddra_name)) %>% rename(Adverse_Event = meddra_name)"},{"path":"https://docs.ropensci.org/dbparser/articles/drugbank_nside.html","id":"polypharmacy-analysis-drug-drug-interactions","dir":"Articles","previous_headings":"","what":"5. Polypharmacy Analysis: Drug-Drug Interactions","title":"Integrated Pharmacovigilance: Linking DrugBank, OnSIDES, and TWOSIDES","text":"New Insight: happens Leuprolide Calcitriol taken together? query integrated_data$drug_drug_interactions table, populated merge_drugbank_twosides. table allows us look interactions DrugBank ID, using enriched columns provided merge.","code":"# Look for interactions where Drug 1 is Leuprolide AND Drug 2 is Calcitriol (or vice versa) interaction_data <- final_db$integrated_data$drug_drug_interactions %>% filter( (drugbank_id_1 == target_drug_id & drugbank_id_2 == target_drug_id_2) | (drugbank_id_1 == target_drug_id_2 & drugbank_id_2 == target_drug_id) ) %>% arrange(desc(as.numeric(PRR))) %>% select(drug_name_1, drug_name_2, condition_concept_name, PRR, mean_reporting_frequency) print(interaction_data) #> drug_name_1 drug_name_2 condition_concept_name PRR #> #> 1: Leuprolide Calcitriol Platelet count decreased 15 #> 2: Leuprolide Calcitriol Disturbance in attention 5 #> 3: Leuprolide Calcitriol Haemorrhage 5 #> mean_reporting_frequency #> #> 1: 0.0909091 #> 2: 0.0303030 #> 3: 0.0303030"},{"path":"https://docs.ropensci.org/dbparser/articles/drugbank_nside.html","id":"visualization-of-interaction-risks","dir":"Articles","previous_headings":"5. Polypharmacy Analysis: Drug-Drug Interactions","what":"5.1 Visualization of Interaction Risks","title":"Integrated Pharmacovigilance: Linking DrugBank, OnSIDES, and TWOSIDES","text":"can visualize specific adverse events associated combination, ranked Proportional Reporting Ratio (PRR) (measure signal strength).","code":"if(nrow(interaction_data) > 0) { # Prepare data for canvasXpress cx_int_data <- data.frame(PRR = as.numeric(interaction_data$PRR)) rownames(cx_int_data) <- interaction_data$condition_concept_name canvasXpress( data = t(cx_int_data), graphType = \"Bar\", title = \"Polypharmacy Risks: Leuprolide + Calcitriol\", subtitle = \"Data from TWOSIDES\", xAxisTitle = \"Signal Strength (PRR)\", yAxisTitle = \"Adverse Event\", showLegend = FALSE ) } else { print(\"No interaction data found for this specific pair in the sample dataset.\") }"},{"path":"https://docs.ropensci.org/dbparser/articles/drugbank_nside.html","id":"comparative-overview","dir":"Articles","previous_headings":"","what":"6. Comparative Overview","title":"Integrated Pharmacovigilance: Linking DrugBank, OnSIDES, and TWOSIDES","text":"Finally, can view single-drug profiles alongside interaction profile see new risks emerge.","code":"# 1. Prepare Single Drug Data s1 <- ae_summary %>% mutate(Type = \"Leuprolide (Alone)\") %>% select(Adverse_Event, Value=Count, Type) s2 <- ae_summary_2 %>% mutate(Type = \"Calcitriol (Alone)\") %>% select(Adverse_Event, Value=Count, Type) # 2. Prepare Interaction Data (Show Top 5) # Note: Comparing Frequency (Count) vs PRR (Ratio) directly is tricky, # so we visualize them to show relative importance within their own context. s3 <- interaction_data %>% head(5) %>% mutate(Type = \"Interaction (PRR)\", Value = as.numeric(PRR)) %>% select(Adverse_Event = condition_concept_name, Value, Type) # 3. Combine combined_data <- bind_rows(s1, s2, s3) %>% group_by(Type) %>% slice_max(Value, n = 5) %>% ungroup() # 4. Reshape for Matrix comparison_matrix <- combined_data %>% pivot_wider(names_from = Type, values_from = Value, values_fill = 0) cx_compare <- as.data.frame(comparison_matrix[, -1]) rownames(cx_compare) <- comparison_matrix$Adverse_Event # 5. Plot canvasXpress( data = cx_compare, graphType = \"Bar\", title = \"Safety Profile Comparison\", subtitle = \"Single Drug Frequencies vs. Interaction PRR\", xAxisTitle = \"Value\", yAxisTitle = \"Adverse Event\", legendPosition = \"right\" )"},{"path":"https://docs.ropensci.org/dbparser/articles/drugbank_nside.html","id":"conclusion","dir":"Articles","previous_headings":"","what":"7. Conclusion","title":"Integrated Pharmacovigilance: Linking DrugBank, OnSIDES, and TWOSIDES","text":"analysis highlights power dbparser integration ecosystem: DrugBank provided identity mechanism Leuprolide Calcitriol. OnSIDES quantified individual side effects (e.g., Pain, Hot Flushes). TWOSIDES revealed specific risks drugs combined (e.g., Anaemia). chaining merge_drugbank_onsides merge_drugbank_twosides, researchers can perform comprehensive safety assessments across mechanistic, real-world, polypharmacy domains just lines R code.","code":""},{"path":"https://docs.ropensci.org/dbparser/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Mohammed Ali. Author, maintainer. Ali Ezzat. Author. Hao Zhu. Reviewer. Emma Mendelsohn. Reviewer.","code":""},{"path":"https://docs.ropensci.org/dbparser/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Mohammed Ali, Ali Ezzat (). dbparser: DrugBank Database XML Parser. R package version 2.2.1.9000.","code":"@Manual{, title = {DrugBank Database XML Parser}, author = {Mohammed Ali and Ali Ezzat}, organization = {Interstellar for Consultinc inc.}, note = {R package version 2.2.1.9000}, url = {https://CRAN.R-project.org/package=dbparser}, }"},{"path":[]},{"path":"https://docs.ropensci.org/dbparser/CODE_OF_CONDUCT.html","id":"our-pledge","dir":"","previous_headings":"","what":"Our Pledge","title":"Contributor Covenant Code of Conduct","text":"interest fostering open welcoming environment, contributors maintainers pledge making participation project community harassment-free experience everyone, regardless age, body size, disability, ethnicity, sex characteristics, gender identity expression, level experience, education, socio-economic status, nationality, personal appearance, race, religion, sexual identity orientation.","code":""},{"path":"https://docs.ropensci.org/dbparser/CODE_OF_CONDUCT.html","id":"our-standards","dir":"","previous_headings":"","what":"Our Standards","title":"Contributor Covenant Code of Conduct","text":"Examples behavior contributes creating positive environment include: Using welcoming inclusive language respectful differing viewpoints experiences Gracefully accepting constructive criticism Focusing best community Showing empathy towards community members Examples unacceptable behavior participants include: use sexualized language imagery unwelcome sexual attention advances Trolling, insulting/derogatory comments, personal political attacks Public private harassment Publishing others’ private information, physical electronic address, without explicit permission conduct reasonably considered inappropriate professional setting","code":""},{"path":"https://docs.ropensci.org/dbparser/CODE_OF_CONDUCT.html","id":"our-responsibilities","dir":"","previous_headings":"","what":"Our Responsibilities","title":"Contributor Covenant Code of Conduct","text":"Project maintainers responsible clarifying standards acceptable behavior expected take appropriate fair corrective action response instances unacceptable behavior. Project maintainers right responsibility remove, edit, reject comments, commits, code, wiki edits, issues, contributions aligned Code Conduct, ban temporarily permanently contributor behaviors deem inappropriate, threatening, offensive, harmful.","code":""},{"path":"https://docs.ropensci.org/dbparser/CODE_OF_CONDUCT.html","id":"scope","dir":"","previous_headings":"","what":"Scope","title":"Contributor Covenant Code of Conduct","text":"Code Conduct applies within project spaces public spaces individual representing project community. Examples representing project community include using official project e-mail address, posting via official social media account, acting appointed representative online offline event. Representation project may defined clarified project maintainers.","code":""},{"path":"https://docs.ropensci.org/dbparser/CODE_OF_CONDUCT.html","id":"enforcement","dir":"","previous_headings":"","what":"Enforcement","title":"Contributor Covenant Code of Conduct","text":"Instances abusive, harassing, otherwise unacceptable behavior may reported contacting project team mohammed.ali@edu.dsti.institute. complaints reviewed investigated result response deemed necessary appropriate circumstances. project team obligated maintain confidentiality regard reporter incident. details specific enforcement policies may posted separately. Project maintainers follow enforce Code Conduct good faith may face temporary permanent repercussions determined members project’s leadership.","code":""},{"path":"https://docs.ropensci.org/dbparser/CODE_OF_CONDUCT.html","id":"attribution","dir":"","previous_headings":"","what":"Attribution","title":"Contributor Covenant Code of Conduct","text":"Code Conduct adapted Contributor Covenant, version 1.4, available https://www.contributor-covenant.org/version/1/4/code--conduct.html answers common questions code conduct, see https://www.contributor-covenant.org/faq","code":""},{"path":"https://docs.ropensci.org/dbparser/CONTRIBUTING.html","id":null,"dir":"","previous_headings":"","what":"Contributing to dbparser","title":"Contributing to dbparser","text":"First , thanks considering contributing dbparser! 👍 ’s people like make rewarding us - project maintainers - work dbparser. 😊 dbparser open source project, maintained people care. directly funded .","code":""},{"path":"https://docs.ropensci.org/dbparser/CONTRIBUTING.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of conduct","title":"Contributing to dbparser","text":"Please note project released Contributor Code Conduct. participating project agree abide terms.","code":""},{"path":"https://docs.ropensci.org/dbparser/CONTRIBUTING.html","id":"how-you-can-contribute","dir":"","previous_headings":"","what":"How you can contribute","title":"Contributing to dbparser","text":"several ways can contribute project. want know contribute open source projects like one, see Open Source Guide.","code":""},{"path":"https://docs.ropensci.org/dbparser/CONTRIBUTING.html","id":"share-the-love-heart","dir":"","previous_headings":"How you can contribute","what":"Share the love ❤️","title":"Contributing to dbparser","text":"Think dbparser useful? Let others discover , telling person, via Twitter blog post. Using dbparser paper writing? Consider citing .","code":""},{"path":"https://docs.ropensci.org/dbparser/CONTRIBUTING.html","id":"ask-a-question-interrobang","dir":"","previous_headings":"How you can contribute","what":"Ask a question ⁉️","title":"Contributing to dbparser","text":"Using our_package got stuck? Browse documentation see can find solution. Still stuck? Post question issue GitHub. offer user support, ’ll try best address , questions often lead better documentation discovery bugs. Want ask question private? Contact package maintainer email.","code":""},{"path":"https://docs.ropensci.org/dbparser/CONTRIBUTING.html","id":"propose-an-idea-bulb","dir":"","previous_headings":"How you can contribute","what":"Propose an idea 💡","title":"Contributing to dbparser","text":"idea new our_package feature? Take look documentation issue list see isn’t included suggested yet. , suggest idea issue GitHub. can’t promise implement idea, helps : Explain detail work. Keep scope narrow possible. See want contribute code idea well.","code":""},{"path":"https://docs.ropensci.org/dbparser/CONTRIBUTING.html","id":"report-a-bug-bug","dir":"","previous_headings":"How you can contribute","what":"Report a bug 🐛","title":"Contributing to dbparser","text":"Using our_package discovered bug? ’s annoying! Don’t let others experience report issue GitHub can fix . good bug report makes easier us , please include: operating system name version (e.g. Mac OS 10.13.6). details local setup might helpful troubleshooting. Detailed steps reproduce bug.","code":""},{"path":"https://docs.ropensci.org/dbparser/CONTRIBUTING.html","id":"improve-the-documentation-open_book","dir":"","previous_headings":"How you can contribute","what":"Improve the documentation 📖","title":"Contributing to dbparser","text":"Noticed typo website? Think function use better example? Good documentation makes difference, help improve welcome!","code":""},{"path":"https://docs.ropensci.org/dbparser/CONTRIBUTING.html","id":"the-website","dir":"","previous_headings":"How you can contribute > Improve the documentation 📖","what":"The website","title":"Contributing to dbparser","text":"website generated pkgdown. means don’t write html: content pulled together documentation code, vignettes, Markdown files, package DESCRIPTION _pkgdown.yml settings. know way around pkgdown, can propose file change improve documentation. , report issue can point right direction.","code":""},{"path":"https://docs.ropensci.org/dbparser/CONTRIBUTING.html","id":"function-documentation","dir":"","previous_headings":"How you can contribute > Improve the documentation 📖","what":"Function documentation","title":"Contributing to dbparser","text":"Functions described comments near code translated documentation using roxygen2. want improve function description: Go R/ directory code repository. Look file name function. Propose file change update function documentation roxygen comments (starting #').","code":""},{"path":"https://docs.ropensci.org/dbparser/CONTRIBUTING.html","id":"contribute-code-pencil","dir":"","previous_headings":"How you can contribute","what":"Contribute code 📝","title":"Contributing to dbparser","text":"Care fix bugs implement new functionality our_package? Awesome! 👏 look issue list leave comment things want work . See also development guidelines .","code":""},{"path":"https://docs.ropensci.org/dbparser/CONTRIBUTING.html","id":"development-guidelines","dir":"","previous_headings":"","what":"Development guidelines","title":"Contributing to dbparser","text":"try follow GitHub flow development. Fork repo clone computer. learn process, see guide. forked cloned project since worked , pull changes original repo clone using git pull upstream master. Open RStudio project file (.Rproj). Write code. Test code (bonus points adding unit tests). Document code (see function documentation ). Check code devtools::check() aim 0 errors warnings. Commit push changes. Submit pull request.","code":""},{"path":[]},{"path":"https://docs.ropensci.org/dbparser/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"Drugs Databases Parser","text":"Drugs databases vary much formats structures making related data analysis easy job requires lot efforts work two databases together DrugBank, OnSIDES, TWOSIDES. Hence, dbparser package aims parse different public drugs databases single unified format R object called dvobject (stands drugverse object). recent updates, dbparser evolved integration engine, allowing merge mechanistic data (DrugBank) real-world phenotypic data (OnSIDES) drug-drug interaction risks (TWOSIDES). help : working single data object multiple databases different formats, using R analysis capabilities easily drugs data, ease transferring data researchers performing required data analysis dvobject storing results object easy manner.","code":""},{"path":"https://docs.ropensci.org/dbparser/index.html","id":"dbparser-in-advanced-research","dir":"","previous_headings":"","what":"dbparser in Advanced Research","title":"Drugs Databases Parser","text":"dbparser serves data infrastructure cutting-edge research: Explainable AI Drug Repurposing: Featured IEEE ICEBE 2025 presentation knowledge graph-based drug discovery (University Technology Sydney collaboration) Systems Pharmacology: Integrated Multipath package pathway modeling Pandemic Response: Enabled rapid COVID-19 therapeutic candidate identification Cancer Research: Supporting SURFACER workflow pan-cancer biomarker detection","code":""},{"path":"https://docs.ropensci.org/dbparser/index.html","id":"dvobject-structure","dir":"","previous_headings":"","what":"dvobject Structure","title":"Drugs Databases Parser","text":"dvobject introduces unified compressed format drugs data. R list object. single database (e.g., DrugBank): contains one following sub-lists: drugs: list data.frames contain drugs information (.e. synonyms, classifications, …) mandatory list salts: data.frame contains drugs salts information products: data.frame commercially available drugs products world references: data.frame articles, links textbooks drugs CETT data cett: list data.frames contain targets, enzymes, carriers transporters information merged database (Integrated Pharmacovigilance): databases merged using merge_drugbank_onsides merge_drugbank_twosides, dvobject becomes nested structure containing: drugbank: mechanistic hub. onsides: side-effect data (FDA labels). twosides: drug-drug interaction data. integrated_data: Enriched tables bridge databases (e.g., linking DrugBank IDs OnSIDES adverse events). metadata: Detailed provenance contained datasets.","code":""},{"path":"https://docs.ropensci.org/dbparser/index.html","id":"drug-databases","dir":"","previous_headings":"","what":"Drug Databases","title":"Drugs Databases Parser","text":"Parsers available following databases (progress list)","code":""},{"path":"https://docs.ropensci.org/dbparser/index.html","id":"id_1-drugbank","dir":"","previous_headings":"Drug Databases","what":"1. DrugBank","title":"Drugs Databases Parser","text":"DrugBank database comprehensive, freely accessible, online database containing information drugs drug targets. bioinformatics cheminformatics resource, DrugBank combines detailed drug (.e. chemical, pharmacological pharmaceutical) data comprehensive drug target (.e. sequence, structure, pathway) information. information DrugBank can found . raw form, DrugBank database single XML file. Users must create account DrugBank request permission download database. Note may take couple days. dbparser package parses DrugBank XML database R tibbles can explored analyzed user, check tutorial details. waiting access DrugBank database, intend deep dive data, may wish use dbdataset package, contains DrugBank database already parsed dvobject. Note large package exceeds limit set CRAN. available GitHub. dbparser tested DrugBank versions 5.1.0 5.1.12 successfully. find errors versions version please submit issue .","code":""},{"path":"https://docs.ropensci.org/dbparser/index.html","id":"id_2-onsides-the-phenotype","dir":"","previous_headings":"Drug Databases","what":"2. OnSIDES (The Phenotype)","title":"Drugs Databases Parser","text":"OnSIDES provides adverse drug events extracted thousands FDA drug labels using machine learning. * Parser: parseOnSIDES() * Input: Directory containing OnSIDES CSV files.","code":""},{"path":"https://docs.ropensci.org/dbparser/index.html","id":"id_3-twosides-polypharmacy","dir":"","previous_headings":"Drug Databases","what":"3. TWOSIDES (Polypharmacy)","title":"Drugs Databases Parser","text":"TWOSIDES provides data drug-drug interactions adverse events arise two drugs taken together. * Parser: parseTWOSIDES() * Input: TWOSIDES.csv.gz file.","code":""},{"path":"https://docs.ropensci.org/dbparser/index.html","id":"quick-start-integration-pipeline","dir":"","previous_headings":"","what":"Quick Start: Integration Pipeline","title":"Drugs Databases Parser","text":"power dbparser lies ability chain parsers mergers together. can build complete pharmacovigilance dataset: detailed case study, please refer Integrated Pharmacovigilance Vignette.","code":"library(dbparser) library(dplyr) # 1. Parse the raw databases drugbank_db <- parseDrugBank(\"data/drugbank.xml\") onsides_db <- parseOnSIDES(\"data/onsides/\") twosides_db <- parseTWOSIDES(\"data/TWOSIDES.csv.gz\") # 2. Build the Integrated Knowledge Graph # DrugBank serves as the hub. We chain the merges. final_db <- drugbank_db %>% merge_drugbank_onsides(onsides_db) %>% merge_drugbank_twosides(twosides_db) # 3. Analyze Results # Example: Accessing the enriched drug-drug interaction table head(final_db$integrated_data$drug_drug_interactions)"},{"path":"https://docs.ropensci.org/dbparser/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Drugs Databases Parser","text":"can install released version dbparser CRAN : can install latest updates directly repo","code":"install.packages(\"dbparser\") library(devtools) devtools::install_github(\"ropensci/dbparser\")"},{"path":"https://docs.ropensci.org/dbparser/index.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"Drugs Databases Parser","text":"Please note ‘dbparser’ project released Contributor Code Conduct. contributing project, agree abide terms.","code":""},{"path":"https://docs.ropensci.org/dbparser/index.html","id":"contributing-guide","dir":"","previous_headings":"","what":"Contributing Guide","title":"Drugs Databases Parser","text":"👍🎉 First , thanks taking time contribute! 🎉👍 Please review Contributing Guide.","code":""},{"path":"https://docs.ropensci.org/dbparser/index.html","id":"share-the-love-heart","dir":"","previous_headings":"","what":"Share the love ❤️","title":"Drugs Databases Parser","text":"Think dbparser useful? Let others discover , telling person, via Twitter blog post. Using dbparser paper writing? Consider citing ","code":"citation(\"dbparser\") #> To cite dbparser in publications use: #> #> Mohammed Ali, Ali Ezzat (). dbparser: DrugBank Database XML Parser. #> R package version 2.2.1.9000. #> #> A BibTeX entry for LaTeX users is #> #> @Manual{, #> title = {DrugBank Database XML Parser}, #> author = {Mohammed Ali and Ali Ezzat}, #> organization = {Interstellar for Consultinc inc.}, #> note = {R package version 2.2.1.9000}, #> url = {https://CRAN.R-project.org/package=dbparser}, #> }"},{"path":[]},{"path":[]},{"path":"https://docs.ropensci.org/dbparser/Intersterllar_Next.html","id":"february-2025---june-2025","dir":"","previous_headings":"Business Plan: dbparser Commercialization & Research Strategy","what":"February 2025 - June 2025","title":"Interstellar Consultation Services","text":"Prepared : Mohammed Ali, CTODate: February 2025Purpose: Strategic alignment document team discussion","code":""},{"path":[]},{"path":"https://docs.ropensci.org/dbparser/Intersterllar_Next.html","id":"current-state","dir":"","previous_headings":"EXECUTIVE SUMMARY","what":"Current State","title":"Interstellar Consultation Services","text":"dbparser: 50K+ downloads, rOpenSci-reviewed, cited 10+ publications (Nature, IEEE, etc.) Team strength: Proven R&D capabilities, academic credibility, research partnerships Challenge: Need transition “impressive open-source project” “sustainable business” maintaining research credibility","code":""},{"path":"https://docs.ropensci.org/dbparser/Intersterllar_Next.html","id":"strategic-decision","dir":"","previous_headings":"EXECUTIVE SUMMARY","what":"Strategic Decision","title":"Interstellar Consultation Services","text":"pursue dual-track strategy: - Track (Research): Leverage grants fund infrastructure development build credibility - Track B (Commercial): Generate immediate revenue services preparing platform scale","code":""},{"path":"https://docs.ropensci.org/dbparser/Intersterllar_Next.html","id":"id_90-day-goals-feb-april-2025","dir":"","previous_headings":"EXECUTIVE SUMMARY","what":"90-Day Goals (Feb-April 2025)","title":"Interstellar Consultation Services","text":"Submit 2 major grant applications (NIH R03, ARC PostDoc) Support PhD RO1 completion (critical research milestone) Generate first commercial revenue ($10K-50K signed contracts) Create foundational commercial assets (validation docs, outreach materials)","code":""},{"path":"https://docs.ropensci.org/dbparser/Intersterllar_Next.html","id":"id_12-month-vision-by-feb-2026","dir":"","previous_headings":"EXECUTIVE SUMMARY","what":"12-Month Vision (By Feb 2026)","title":"Interstellar Consultation Services","text":"Research: 2-3 grants funded, PhD extended PostDoc, methodology validated Commercial: 5-10 paying customers, $100K-250K revenue, dbparser Pro established Platform: DrugVerse product vision validated, foundation built scale","code":""},{"path":[]},{"path":"https://docs.ropensci.org/dbparser/Intersterllar_Next.html","id":"id_11-the-core-insight","dir":"","previous_headings":"SECTION 1: STRATEGIC FRAMEWORK","what":"1.1 The Core Insight","title":"Interstellar Consultation Services","text":"unique market position academic tools lack: competitive moat: bridge research → infrastructure → productization way ’s rare computational biology.","code":""},{"path":[]},{"path":"https://docs.ropensci.org/dbparser/Intersterllar_Next.html","id":"what-were-building-3-stage-evolution","dir":"","previous_headings":"SECTION 1: STRATEGIC FRAMEWORK > 1.2 Product Positioning","what":"What We’re Building (3-Stage Evolution):","title":"Interstellar Consultation Services","text":"Near-term focus: Stage 1 (dbparser Pro services)","code":"STAGE 1 (Now - 6 months): dbparser Pro ├── What: Professional services around open-source dbparser ├── Offering: Consulting, integration, validation support ├── Target: Pharma, biotech, CROs, research labs ├── Revenue: $10K-50K per project └── Infrastructure: None needed (we are the product) STAGE 2 (6-12 months): dbparser Pro++ ├── What: Productized services ├── Offering: Support subscriptions, training, validation packages ├── Target: Same customers, but at scale ├── Revenue: $5K-25K/year per organization └── Infrastructure: Support portal, documentation, training materials STAGE 3 (12-24 months): DrugVerse Platform ├── What: Full SaaS platform (web-based drug repurposing tool) ├── Offering: Freemium → Pro → Enterprise tiers ├── Target: Researchers + pharma + biotech (broader market) ├── Revenue: $49-499/month subscriptions + enterprise deals └── Infrastructure: Shiny web app, backend infrastructure, hosting"},{"path":[]},{"path":"https://docs.ropensci.org/dbparser/Intersterllar_Next.html","id":"research-track-enables-commercial-track","dir":"","previous_headings":"SECTION 1: STRATEGIC FRAMEWORK > 1.3 Why This Strategy Makes Sense","what":"Research Track Enables Commercial Track:","title":"Interstellar Consultation Services","text":"Grants fund infrastructure development (DisGeNET integration, Shiny app, Python version) PhD research validates methodology (creates “proof points” sales) Publications build credibility (essential pharma customers) Academic reputation generates inbound leads (papers citing us)","code":""},{"path":"https://docs.ropensci.org/dbparser/Intersterllar_Next.html","id":"commercial-track-validates-research-track","dir":"","previous_headings":"SECTION 1: STRATEGIC FRAMEWORK > 1.3 Why This Strategy Makes Sense","what":"Commercial Track Validates Research Track:","title":"Interstellar Consultation Services","text":"Customer feedback reveals features matter Revenue funds development grants Industry relationships enable applied research Case studies strengthen future grant applications","code":""},{"path":"https://docs.ropensci.org/dbparser/Intersterllar_Next.html","id":"theyre-complementary-not-competitive","dir":"","previous_headings":"SECTION 1: STRATEGIC FRAMEWORK > 1.3 Why This Strategy Makes Sense","what":"They’re Complementary, Not Competitive:","title":"Interstellar Consultation Services","text":"Open-source dbparser = lead generation engine Grants = fuel infrastructure development Services = immediate revenue platform scales Platform = long-term value creation","code":""},{"path":[]},{"path":[]},{"path":"https://docs.ropensci.org/dbparser/Intersterllar_Next.html","id":"objective","dir":"","previous_headings":"SECTION 2: DUAL-TRACK EXECUTION PLAN > 2.1 TRACK A: Research & Grants","what":"Objective:","title":"Interstellar Consultation Services","text":"Secure $150K-400K grant funding 12 months fund infrastructure development, validate methodology, build credibility.","code":""},{"path":[]},{"path":"https://docs.ropensci.org/dbparser/Intersterllar_Next.html","id":"nih-r03-strategy-confirmed-structure","dir":"","previous_headings":"SECTION 2: DUAL-TRACK EXECUTION PLAN > 2.1 TRACK A: Research & Grants","what":"NIH R03 Strategy (Confirmed Structure):","title":"Interstellar Consultation Services","text":"Application Focus: “Computational Infrastructure Multi-Database Drug Repurposing Research” Specific Aims: 1. Aim 1 (40% effort): Expand database coverage - DisGeNET integration (gene-disease associations) - SIDER updates (side effects database) - ChEMBL integration (bioactivity data) - Deliverable: dbparser 2.3.0 3 new databases Shiny web application non-R users Pre-loaded datasets (expand dbdataset) Interactive visualization Deliverable: Web-based exploration tool (beta) Training materials (workshops, video tutorials) Validation documentation (pharma use) Contributor guidelines Deliverable: Community infrastructure + validation docs Budget: $50K/year × 2 years = $100K total Strategic Decisions: - ✅ Include Shiny app (addresses “broader impact”) - ✅ Include validation docs (pharma use case) - ❌ Exclude Python version (scope creep, competitive risk) - ❌ Reserve Python future grants customer-funded development Rationale excluding Python: - R03 “small grant” - Python consume 30-40% budget - Creates 2x maintenance burden indefinitely - Shiny app already addresses non-R users - Python version better suited larger grants (NSF CSSI, CZI) - Can document “future work” without committing now","code":""},{"path":"https://docs.ropensci.org/dbparser/Intersterllar_Next.html","id":"arc-postdoc-strategy","dir":"","previous_headings":"SECTION 2: DUAL-TRACK EXECUTION PLAN > 2.1 TRACK A: Research & Grants","what":"ARC PostDoc Strategy:","title":"Interstellar Consultation Services","text":"Led : UTS team (support partner) Research Extension Focus: - Extend PhD methodology multi-target neurological diseases - Clinical validation real-world datasets - LLM integration literature extraction - Explainability framework refinement Role: - Infrastructure provider (dbparser, data pipelines) - Co-author publications - Commercialization partner - Potential Sydney branch establishment (via UTS Startup incubator) Commercialization Pathway (proposal): > “Research outputs commercialized Interstellar Consultation Services, R&D accelerator proven track record (dbparser: 50K downloads, 10+ publications). Interstellar transform validated algorithms production-ready DrugVerse platform pharma/biotech market.”","code":""},{"path":"https://docs.ropensci.org/dbparser/Intersterllar_Next.html","id":"phd-support-ongoing","dir":"","previous_headings":"SECTION 2: DUAL-TRACK EXECUTION PLAN > 2.1 TRACK A: Research & Grants","what":"PhD Support (Ongoing):","title":"Interstellar Consultation Services","text":"Commitment: Support UTS team complete research objectives Current Status: - RO1: progress (target completion: Feb-March 2025) - RO2: Upcoming (March-June 2025) - Output: Papers explicitly mentioning dbparser + framework Strategic Value: - Research validates methodology - Publications generate credibility - Papers cite dbparser (lead generation) - Creates proof points commercial sales","code":""},{"path":[]},{"path":"https://docs.ropensci.org/dbparser/Intersterllar_Next.html","id":"objective-1","dir":"","previous_headings":"SECTION 2: DUAL-TRACK EXECUTION PLAN > 2.2 TRACK B: Commercial Revenue","what":"Objective:","title":"Interstellar Consultation Services","text":"Generate $50K-150K revenue (Feb-Dec 2025) professional services building foundation platform scale.","code":""},{"path":"https://docs.ropensci.org/dbparser/Intersterllar_Next.html","id":"phase-1-dbparser-pro-services-model","dir":"","previous_headings":"SECTION 2: DUAL-TRACK EXECUTION PLAN > 2.2 TRACK B: Commercial Revenue","what":"Phase 1: dbparser Pro (Services Model)","title":"Interstellar Consultation Services","text":"’re Selling: Key Point: open-source software (dbparser), different service levels.","code":""},{"path":"https://docs.ropensci.org/dbparser/Intersterllar_Next.html","id":"go-to-market-strategy","dir":"","previous_headings":"SECTION 2: DUAL-TRACK EXECUTION PLAN > 2.2 TRACK B: Commercial Revenue","what":"Go-to-Market Strategy:","title":"Interstellar Consultation Services","text":"Target Segments (Priority Order): : 10+ research teams published using dbparser : Warmest leads (already trust work) Approach: Personal outreach offering free consultation Expected conversion: 10 emails → 5 responses → 2 pilots → 1 customer Timeline: Outreach April 2025 (Ramadan) : Pharmaceutical companies bioinformatics departments : Need validation support regulatory compliance Approach: LinkedIn + warm introductions + conferences Expected conversion: Longer sales cycle (60-90 days) Timeline: April-June 2025 : Contract research organizations, funded startups : Need fast data processing, reproducible pipelines Approach: Industry events, RFP responses Timeline: May-Dec 2025","code":""},{"path":"https://docs.ropensci.org/dbparser/Intersterllar_Next.html","id":"validation-documentation-package-key-sellable-asset","dir":"","previous_headings":"SECTION 2: DUAL-TRACK EXECUTION PLAN > 2.2 TRACK B: Commercial Revenue","what":"Validation Documentation Package (Key Sellable Asset):","title":"Interstellar Consultation Services","text":"Problem Pharma : - FDA requires software validation tool used regulatory submissions - Pharma QA teams don’t know validate open-source R packages - Creating validation docs internally takes 3-6 months Solution: Pre-built validation package template dbparser, customized per client. ’s Included: Development Strategy: - Now (Feb): Create minimal version (test cases + verification guide) - 10 hours - NIH Grant: Include deliverable Aim 3 (sustainability) - First Pharma Customer: Charge $12K-15K full customized package - 40-60 hours - Future Customers: Reuse template (80% , 20% customized) - 8-12 hours Revenue Potential: $12K-15K per pharma customer, high reusability","code":""},{"path":"https://docs.ropensci.org/dbparser/Intersterllar_Next.html","id":"outreach-strategy-april-2025","dir":"","previous_headings":"SECTION 2: DUAL-TRACK EXECUTION PLAN > 2.2 TRACK B: Commercial Revenue","what":"Outreach Strategy (April 2025):","title":"Interstellar Consultation Services","text":"Timing Rationale: - Feb: NIH R03 sprint (distractions) - March: Ramadan (lighter work, strategic planning) - April: Full energy outreach sales Preparation (Late February - 2 weeks): Initial outreach template Follow-template Proposal framework template Minimal version (test cases, verification guide, SOP template) inclusion NIH proposal sharing prospects One-page services overview Three tiers clear deliverables Use prospect conversations Execution (April - 4 weeks): Week 1: Initial outreach - Email 10 authors papers citing dbparser - Personalized approach (reference specific research) - Offer: Free 30-min consultation data integration challenges Week 2-3: Consultation calls - 30-minute discovery calls (understand needs, qualify budget) - Document pain points requirements - Identify service tier fits Week 3-4: Proposal & closing - Send customized proposals qualified leads - Follow within 48 hours - Negotiate scope pricing - Close first 1-2 deals Success Metrics: - Target: 1-2 signed contracts end April ($10K-50K total value) - Pipeline: 3-5 additional qualified prospects Q2 - Validation: Pricing confirmed market response","code":""},{"path":[]},{"path":"https://docs.ropensci.org/dbparser/Intersterllar_Next.html","id":"february-2025-weeks-1-4","dir":"","previous_headings":"SECTION 3: 90-DAY EXECUTION TIMELINE","what":"February 2025 (Weeks 1-4)","title":"Interstellar Consultation Services","text":"Deliverables End February: - ✅ NIH R03 submitted - ✅ PhD RO1 complete near-complete - ✅ Validation docs (minimal version) created - ✅ Outreach templates prepared - ✅ Pricing sheet created Total Time: ~65 hours (16h/week average)","code":""},{"path":"https://docs.ropensci.org/dbparser/Intersterllar_Next.html","id":"march-2025-weeks-5-8---ramadan-period","dir":"","previous_headings":"SECTION 3: 90-DAY EXECUTION TIMELINE","what":"March 2025 (Weeks 5-8) - Ramadan Period","title":"Interstellar Consultation Services","text":"Notes: - Lower intensity Ramadan (10-12h/week vs. 16h/week) - Focus strategic planning vs. intensive execution - ARC deadline timing depends UTS confirmation Deliverables End March: - ✅ ARC PostDoc proposal ready (submit deadline confirmed) - ✅ DrugVerse product vision documented - ✅ CZI/NSF grant opportunities researched - ✅ PhD RO2 planning complete Total Time: ~42 hours (10.5h/week average)","code":""},{"path":"https://docs.ropensci.org/dbparser/Intersterllar_Next.html","id":"april-2025-weeks-9-12","dir":"","previous_headings":"SECTION 3: 90-DAY EXECUTION TIMELINE","what":"April 2025 (Weeks 9-12)","title":"Interstellar Consultation Services","text":"Deliverables End April: - ✅ 10 outreach emails sent - ✅ 5-7 consultation calls completed - ✅ 2-3 proposals delivered - ✅ 1-2 signed contracts ($10K-50K total) - ✅ DisGeNET integration progress - ✅ PhD RO2 progressing Total Time: ~64 hours (16h/week average)","code":""},{"path":[]},{"path":[]},{"path":[]},{"path":"https://docs.ropensci.org/dbparser/Intersterllar_Next.html","id":"id_43-ip--partnership-clarity","dir":"","previous_headings":"SECTION 4: ROLES & RESPONSIBILITIES","what":"4.3 IP & Partnership Clarity","title":"Interstellar Consultation Services","text":": - ✅ dbparser (MIT open source, maintain) - ✅ dbdataset (package) - ✅ periscope2 (package) - ✅ DrugVerse brand platform (Interstellar owns) - ✅ Commercial relationships customer contracts UTS Team Owns: - ✅ XAI algorithm methodology (PhD research) - ✅ Research publications (co-authors) - ✅ Patents (filed - likely algorithmic innovations) Joint Arrangements: - Written agreement exists UTS team members (university) - Interstellar = commercialization partner - UTS Sydney branch = potential joint venture (via UTS Startup incubator) - Neither party proceeds independently without Protection Strategy: - control data infrastructure layer → need dbparser - control algorithmic innovation → need XAI methodology - Together create value neither alone - Platform GTM , algorithms can licensed","code":""},{"path":[]},{"path":"https://docs.ropensci.org/dbparser/Intersterllar_Next.html","id":"id_51-revenue-projections-conservative","dir":"","previous_headings":"SECTION 5: FINANCIAL PROJECTIONS","what":"5.1 Revenue Projections (Conservative)","title":"Interstellar Consultation Services","text":"Assumptions: - Low case: 1 customer/quarter $10K average - Mid case: 2-3 customers/quarter $15-25K average - High case: 3-5 customers/quarter $20-30K average","code":""},{"path":"https://docs.ropensci.org/dbparser/Intersterllar_Next.html","id":"id_52-grant-funding-projections","dir":"","previous_headings":"SECTION 5: FINANCIAL PROJECTIONS","what":"5.2 Grant Funding Projections","title":"Interstellar Consultation Services","text":"Notes: - Conservative success rates used - NIH R03 likely (proven track record, clear fit) - Grants fund infrastructure, operations - Don’t depend grants survival","code":""},{"path":"https://docs.ropensci.org/dbparser/Intersterllar_Next.html","id":"id_53-investment-needs","dir":"","previous_headings":"SECTION 5: FINANCIAL PROJECTIONS","what":"5.3 Investment Needs","title":"Interstellar Consultation Services","text":"Current State: - AG business provides baseline revenue (covers personal) - immediate cash crisis - runway execute 90-day plan Potential Uses Grant Funding (awarded): Potential Uses Commercial Revenue:","code":""},{"path":[]},{"path":[]},{"path":"https://docs.ropensci.org/dbparser/Intersterllar_Next.html","id":"id_62-contingency-plans","dir":"","previous_headings":"SECTION 6: RISK ANALYSIS & MITIGATION","what":"6.2 Contingency Plans","title":"Interstellar Consultation Services","text":"NIH R03 Rejected: - Resubmit June October cycle (reviewer feedback) - Use commercial revenue fund DisGeNET integration - Focus smaller grants (R21, foundation grants) Commercial Traction June: - Reassess pricing (may high/low) - Expand outreach beyond citing papers - Consider pivot training/workshops (easier sale) - Apply lessons learned DrugVerse positioning PhD PostDoc Doesn’t Materialize: - DrugVerse still viable (idea predates UTS collaboration) - Continue research consultants vs. employees - Pursue research partnerships - Focus commercial platform vs. cutting-edge research Capacity Constraints Hit: - Hire Fellows grant money (funded) - Engage Singapore partner technical work - Menna transitions part-time Interstellar - Pause lower-priority initiatives (e.g., CZI grant)","code":""},{"path":[]},{"path":"https://docs.ropensci.org/dbparser/Intersterllar_Next.html","id":"id_71-90-day-metrics-feb-april-2025","dir":"","previous_headings":"SECTION 7: SUCCESS METRICS","what":"7.1 90-Day Metrics (Feb-April 2025)","title":"Interstellar Consultation Services","text":"Research Track: - ✅ NIH R03 submitted (Feb 16) - CRITICAL - ✅ ARC PostDoc outlined/submitted (March/June) - HIGH - ✅ PhD RO1 complete - HIGH - ✅ Output paper progress (mentions dbparser + framework) - MEDIUM Commercial Track: - ✅ 1-2 signed contracts ($10K-50K total) - CRITICAL - ✅ 3-5 qualified prospects pipeline - HIGH - ✅ Validation docs created (minimal version) - MEDIUM - ✅ Outreach templates tested refined - MEDIUM Infrastructure: - ✅ DisGeNET integration started - MEDIUM - ✅ Product vision documented - MEDIUM - ✅ Team alignment strategy - HIGH","code":""},{"path":"https://docs.ropensci.org/dbparser/Intersterllar_Next.html","id":"id_72-12-month-metrics-by-feb-2026","dir":"","previous_headings":"SECTION 7: SUCCESS METRICS","what":"7.2 12-Month Metrics (By Feb 2026)","title":"Interstellar Consultation Services","text":"Research Track: - 1-2 grants funded ($100K-400K total) - 2-3 publications dbparser citations - PhD extended PostDoc (equivalent continuation) - Methodology validated peer-reviewed venues Commercial Track: - 5-10 paying customers - $100K-250K revenue (cumulative) - dbparser Pro service offerings established - 2-3 case studies/testimonials Platform Track: - DrugVerse beta launched (select users) - Product-market fit validated - Foundation built 2026 scale - Sydney branch discussion advanced (via UTS Startup)","code":""},{"path":[]},{"path":"https://docs.ropensci.org/dbparser/Intersterllar_Next.html","id":"id_81-grant-strategy","dir":"","previous_headings":"SECTION 8: OPEN QUESTIONS FOR TEAM DISCUSSION","what":"8.1 Grant Strategy","title":"Interstellar Consultation Services","text":"Question 1: NIH R03 Aims - Final Review - 3-aim structure make sense? - add anything Aim 3 (sustainability)? - excluding Python version right decision? Question 2: Grant Prioritization - Focus NIH + ARC (confirmed) - pursue CZI/NSF June, wait 2026? - grant opportunities ’re missing?","code":""},{"path":"https://docs.ropensci.org/dbparser/Intersterllar_Next.html","id":"id_82-commercial-strategy","dir":"","previous_headings":"SECTION 8: OPEN QUESTIONS FOR TEAM DISCUSSION","what":"8.2 Commercial Strategy","title":"Interstellar Consultation Services","text":"Question 3: Pricing Validation - $10K-50K feel right professional services? - offer lower-tier option ($5K support-)? - position “just use free dbparser”? Question 4: Target Segment Priority - Agree “citing papers first” approach? - add near-term segments? - much effort pharma vs. biotech vs. academic? Question 5: Validation Docs - minimal version (10 hours) sufficient Feb? - create full version proactively, wait customer? - can review accuracy (pharma QA perspective)?","code":""},{"path":"https://docs.ropensci.org/dbparser/Intersterllar_Next.html","id":"id_83-product--platform","dir":"","previous_headings":"SECTION 8: OPEN QUESTIONS FOR TEAM DISCUSSION","what":"8.3 Product & Platform","title":"Interstellar Consultation Services","text":"Question 6: DrugVerse Timing - Agree “services first, platform later” sequence? - start platform development? (X customers? X revenue? Grant funded?) - build Shiny app part NIH grant (Aim 2), commercial product? Question 7: Python Version - Confirm: NIH R03, document future work? - near-term customer realistically pay Python port? - UTS PhD research require Python integration sooner? Question 8: Branding & Naming - “dbparser Pro” clear enough commercial offering? - Keep “DrugVerse” platform name, brainstorm alternatives? - need dedicated branding/positioning work?","code":""},{"path":"https://docs.ropensci.org/dbparser/Intersterllar_Next.html","id":"id_84-team--partnerships","dir":"","previous_headings":"SECTION 8: OPEN QUESTIONS FOR TEAM DISCUSSION","what":"8.4 Team & Partnerships","title":"Interstellar Consultation Services","text":"Question 9: UTS Relationship - current written agreement sufficient commercialization? - need MOU UTS team (vs. individuals)? - formalize Sydney branch opportunity? Question 10: Singapore Partner Role - specific support can provide next 90 days? - involved customer calls/proposals? - compensate (equity, revenue share, consulting fees)? Question 11: Hiring Strategy - NIH funded, hire developer first sales/BD first? - engage Fellows sooner (even without grant)? - Menna transition part-time Interstellar role?","code":""},{"path":"https://docs.ropensci.org/dbparser/Intersterllar_Next.html","id":"id_85-timeline--capacity","dir":"","previous_headings":"SECTION 8: OPEN QUESTIONS FOR TEAM DISCUSSION","what":"8.5 Timeline & Capacity","title":"Interstellar Consultation Services","text":"Question 12: Ramadan Planning - March plan realistic given reduced capacity? - shift March priorities April? - can delegated period? Question 13: Outreach Timing - Confirm April outreach (Ramadan)? - prep March, execute May? - can support outreach execution? Question 14: Prioritization - capacity gets tight, drops first? - Option : CZI/NSF grant research - Option B: DisGeNET integration - Option C: DrugVerse product planning - Option D: Commercial outreach","code":""},{"path":[]},{"path":"https://docs.ropensci.org/dbparser/Intersterllar_Next.html","id":"id_91-immediate-this-week---feb-week-1","dir":"","previous_headings":"SECTION 9: NEXT STEPS & ACTION ITEMS","what":"9.1 Immediate (This Week - Feb Week 1)","title":"Interstellar Consultation Services","text":"Action Items:","code":""},{"path":"https://docs.ropensci.org/dbparser/Intersterllar_Next.html","id":"id_92-february-priorities-locked","dir":"","previous_headings":"SECTION 9: NEXT STEPS & ACTION ITEMS","what":"9.2 February Priorities (Locked)","title":"Interstellar Consultation Services","text":"Week 1-2: NIH R03 Sprint - Draft specific aims using structure document - Budget allocation ($50K × 2 years) - Impact statement (democratizing access drug data) - Deadline: Feb 16 submission Week 3-4: PhD Support + Prep - Support UTS RO1 completion - Create minimal validation docs (10 hours) - Create outreach email templates (2 hours) - Create pricing one-pager (1 hour)","code":""},{"path":"https://docs.ropensci.org/dbparser/Intersterllar_Next.html","id":"id_93-march-priorities-tentative---pending-team-discussion","dir":"","previous_headings":"SECTION 9: NEXT STEPS & ACTION ITEMS","what":"9.3 March Priorities (Tentative - Pending Team Discussion)","title":"Interstellar Consultation Services","text":"PostDoc Proposal: - Draft research extension (clinical validation angle) - Commercialization pathway section (Interstellar partnership) - Sydney branch vision - Submit deadline confirmed Strategic Planning: - Document DrugVerse product vision (features, tiers, roadmap) - Research CZI/NSF grant requirements - Update LinkedIn/website recent accomplishments","code":""},{"path":"https://docs.ropensci.org/dbparser/Intersterllar_Next.html","id":"id_94-april-priorities-tentative---pending-team-discussion","dir":"","previous_headings":"SECTION 9: NEXT STEPS & ACTION ITEMS","what":"9.4 April Priorities (Tentative - Pending Team Discussion)","title":"Interstellar Consultation Services","text":"Commercial Launch: - Email 10 citing papers (personalized outreach) - Consultation calls (5-7 discovery conversations) - Send proposals (2-3 qualified leads) - Close first deals (target 1-2 contracts) Technical Progress: - Begin DisGeNET integration (dbparser 2.3.0) - Support PhD RO2 needed - Refine validation docs based customer feedback","code":""},{"path":[]},{"path":"https://docs.ropensci.org/dbparser/Intersterllar_Next.html","id":"appendix-a-nih-r03-draft-outline","dir":"","previous_headings":"SECTION 10: APPENDICES","what":"Appendix A: NIH R03 Draft Outline","title":"Interstellar Consultation Services","text":"(Can expand section full grant outline needed) Title: “Computational Infrastructure Accelerating Multi-Database Drug Repurposing Research” Specific Aims: (detailed Section 2.1) Significance: - Drug repurposing $30B opportunity - Researchers waste 60-80% time data wrangling - dbparser proven impact (50K downloads, 10+ publications) - Gaps remain: limited database coverage, R-access, lack pharma validation Innovation: - First unified infrastructure 6+ major drug databases - Web-based access non-programmers - Validation framework regulatory use - Community-driven sustainability model Approach: (3 aims outlined)","code":""},{"path":"https://docs.ropensci.org/dbparser/Intersterllar_Next.html","id":"appendix-b-validation-documentation-structure","dir":"","previous_headings":"SECTION 10: APPENDICES","what":"Appendix B: Validation Documentation Structure","title":"Interstellar Consultation Services","text":"(Can expand actual templates needed) Minimal Version (10 hours - Create Feb): 1. Test Cases Document (3-4 pages) 2. Accuracy Verification Guide (2-3 pages) 3. SOP Template (2-3 pages) Full Version (40-60 hours - Create customer pays): 1. , plus: 2. Validation Protocol (10-15 pages) 3. IQ/OQ/PQ Documents (20-30 pages total) 4. Traceability Matrix (3-5 pages) 5. Customization client environment","code":""},{"path":"https://docs.ropensci.org/dbparser/Intersterllar_Next.html","id":"appendix-c-email-outreach-templates","dir":"","previous_headings":"SECTION 10: APPENDICES","what":"Appendix C: Email Outreach Templates","title":"Interstellar Consultation Services","text":"(Can provide full templates needed) Template 1: Initial Outreach - Subject line - Opening (reference paper) - Value proposition (free consultation) - Call action (15-min call) Template 2: Follow-- consultation call - Three-option proposal framework - Next steps Template 3: Proposal - Problem statement (words) - Solution (specific case) - Deliverables (concrete, measurable) - Investment & timeline - Next steps","code":""},{"path":"https://docs.ropensci.org/dbparser/Intersterllar_Next.html","id":"appendix-d-key-resources--links","dir":"","previous_headings":"SECTION 10: APPENDICES","what":"Appendix D: Key Resources & Links","title":"Interstellar Consultation Services","text":"Research Infrastructure: - dbparser: https://github.com/ropensci/dbparser - Documentation: https://docs.ropensci.org/dbparser/ - rOpenSci review: https://github.com/ropensci/software-review/issues/347 Publications: - JOSS paper: (preparation) - IEEE papers XAI: (List links) - Citing papers: (List 10+ publications) Grant Resources: - NIH R03: https://grants.nih.gov/grants/funding/r03.htm - CZI Science: https://chanzuckerberg.com/science/ - NSF CSSI: https://www.nsf.gov/funding/ Company: - Interstellar: https://interstellar4co.com/ - LinkedIn: (Add link)","code":""},{"path":"https://docs.ropensci.org/dbparser/Intersterllar_Next.html","id":"conclusion","dir":"","previous_headings":"","what":"CONCLUSION","title":"Interstellar Consultation Services","text":"business plan outlines pragmatic, dual-track strategy : Leverages strengths: Proven technical infrastructure, academic credibility, research partnerships Generates near-term revenue: Professional services platform scales Builds long-term value: Platform positioned 2026+ growth Manages risk: Multiple funding sources, customer validation major build Respects constraints: 16h/week capacity, Ramadan timing, part-time team next 90 days critical: - Submit 2 major grants (NIH, ARC PostDoc) - Support PhD completion (research credibility) - Land first 1-2 customers (commercial validation) - Build foundational assets (validation docs, outreach materials) Success requires: - ✅ Team alignment priorities - ✅ Ruthless focus critical path - ✅ Discipline say “” distractions - ✅ Flexibility pivot based market feedback strong hand play. Let’s execute clarity confidence. Next Step: Schedule team discussion review plan finalize decisions. Prepared : Mohammed Ali, CTODate: February 2025Version: 1.0 (Team Discussion) DISCUSSION QUESTIONS: 1. resonates? feels ? 2. ’s missing plan? 3. see biggest risks? 4. decisions need discussion? 5. ready execute, need refine ?","code":""},{"path":"https://docs.ropensci.org/dbparser/paper.html","id":null,"dir":"","previous_headings":"","what":"Summary","title":"Summary","text":"dbparser rOpenSci peer-reviewed R package provides unified framework parsing integrating major pharmacological pharmacovigilance databases standardized, analysis-ready R objects. package supports three essential drug information resources: DrugBank [@wishart2018drugbank], OnSIDES [@galeano2022onsides] TWOSIDES [@tatonetti2012data]. database parsed consistent nested list structure called dvobject, preserves complex relational hierarchies enabling seamless cross-database integration. providing high-performance parsing functions, chainable merge operations, comprehensive metadata tracking, dbparser eliminates significant bottleneck computational pharmacology research enables reproducible, large-scale drug safety analyses.","code":""},{"path":"https://docs.ropensci.org/dbparser/paper.html","id":"statement-of-need","dir":"","previous_headings":"","what":"Statement of Need","title":"Summary","text":"Pharmacological research increasingly relies integrating heterogeneous data sources understand drug mechanisms, predict adverse effects, identify drug-drug interactions. Resources DrugBank (comprehensive drug target information), OnSIDES (machine learning-derived side effect predictions), TWOSIDES (drug-drug interaction effects) represent invaluable repositories pharmacological knowledge. However, accessing integrating databases presents substantial technical challenges. database employs distinct file formats structural conventions: DrugBank distributes data deeply nested XML complex entity relationships; OnSIDES provides multiple relational CSV files requiring careful joining; TWOSIDES offers compressed flat files different identifier systems. Researchers typically address inconsistencies developing ad-hoc parsing scripts—approach time-consuming, error-prone, harmful reproducibility. Studies suggest data preprocessing often consumes 60-80% total analysis time pharmacoinformatics workflows [@wickham2014tidy]. R ecosystem, despite strength statistical analysis visualization, lacks dedicated tools pharmacological database integration. Bioconductor [@gentleman2004bioconductor] provides excellent infrastructure genomics data, equivalent standardized framework exists drug databases. dbparser addresses gap providing unified parsing functions, chainable integration workflows, rich metadata preservation, high-performance implementations transform weeks custom development minutes reproducible analysis.","code":""},{"path":"https://docs.ropensci.org/dbparser/paper.html","id":"state-of-the-field","dir":"","previous_headings":"","what":"State of the field","title":"Summary","text":"landscape tools accessing pharmacological databases fragmented across languages lacks comprehensive integration capabilities. surveyed existing solutions developing dbparser found significant gaps justified new development rather contribution existing projects.","code":""},{"path":"https://docs.ropensci.org/dbparser/paper.html","id":"r-ecosystem","dir":"","previous_headings":"","what":"R Ecosystem","title":"Summary","text":"R pharmacology ecosystem limited database integration tools. drugbankr package (archived CRAN since 2019) provided basic DrugBank XML parsing lacked maintenance, testing infrastructure, integration capabilities. supported DrugBank offered framework multi-database workflows. Bioconductor packages AnnotationHub biomaRt excel genomic data integration architecturally designed gene-centric annotations rather drug-centric pharmacological data. data models assume different entity relationships (genes → variants → phenotypes) drug databases require (drugs → targets → pathways → diseases → adverse events). technically possible force pharmacological data frameworks, creates architectural impedance mismatches complicate downstream analyses.","code":""},{"path":"https://docs.ropensci.org/dbparser/paper.html","id":"python-and-other-languages","dir":"","previous_headings":"","what":"Python and Other Languages","title":"Summary","text":"Python tools exist individual databases lack cross-database integration. pydrugbank drugbank-downloader parse DrugBank XML provide standardization layer integrating resources. bioservices accesses web APIs multiple databases focuses real-time queries rather creating integrated, analysis-ready datasets. tools serve different use cases (programmatic access) dbparser (reproducible local analysis). Language barriers also matter: R dominates statistical pharmacology clinical data analysis, making Python-solutions less accessible target community.","code":""},{"path":"https://docs.ropensci.org/dbparser/paper.html","id":"commercial-and-manual-approaches","dir":"","previous_headings":"","what":"Commercial and Manual Approaches","title":"Summary","text":"Commercial platforms like Clarivate Cortellis Certara D360 offer integrated drug data proprietary, expensive (typically $10,000-$50,000+ annually), provide limited reproducibility academic research. Researchers often resort manual approaches: writing custom parsing scripts database, manually reconciling identifiers, creating ad-hoc integration pipelines. solutions non-reproducible, time-intensive, lack quality assurance.","code":""},{"path":"https://docs.ropensci.org/dbparser/paper.html","id":"unique-contribution-of-dbparser","dir":"","previous_headings":"","what":"Unique Contribution of dbparser","title":"Summary","text":"dbparser addresses three critical gaps: (1) Multi-database integration: existing R package provides standardized parsing integration across DrugBank, OnSIDES TWOSIDES unified output structures. (2) Production-quality infrastructure: Achieving 98% test coverage, rOpenSci peer review, comprehensive documentation distinguishes dbparser ad-hoc scripts abandoned packages. (3) Reproducible research focus: Unlike API-based tools retrieve current data, dbparser processes versioned database releases, enabling reproducible analyses critical published research. demonstrated impact—50,000+ downloads, 10+ peer-reviewed publications, downstream package development—validates dbparser fills genuine gap rather duplicating existing functionality.","code":""},{"path":[]},{"path":"https://docs.ropensci.org/dbparser/paper.html","id":"design-philosophy-and-trade-offs","dir":"","previous_headings":"","what":"Design Philosophy and Trade-offs","title":"Summary","text":"dbparser’s architecture reflects three core design decisions emerged extensive experience pharmacological data analysis workflows: Unified dvobject Structure vs. Database-Specific Formats: chose transform databases consistent nested list structure rather preserving native formats. decision trades format-specific optimization dramatically improved interoperability. dvobject maintains relational structure source database providing consistent access patterns, enabling users apply identical analysis code across different data sources. dvobject contains three components: (1) tidy data tables compatible tidyverse ecosystem [@wickham2019welcome], (2) comprehensive metadata (version, parse timestamp, schema information), (3) relationship mappings documenting cross-table linkages. Hub--Spoke Integration Model: Rather attempting --database linking, implemented DrugBank central integration hub. reflects DrugBank’s comprehensive identifier mappings (RxCUI, PubChem, ChEMBL, KEGG) established role reference resource. trade-—requiring DrugBank multi-database analyses—justified substantial reduction identifier reconciliation complexity improved reliability cross-database joins. Chainable Merge Operations: Integration functions designed pipeline composition using magrittr pipe operator, enabling workflows like drugbank_db %>% merge_drugbank_onsides(onsides_db) %>% merge_drugbank_twosides(twosides_db). design prioritizes readability reproducibility marginal performance gains monolithic merge operations.","code":""},{"path":"https://docs.ropensci.org/dbparser/paper.html","id":"architectural-foundation","dir":"","previous_headings":"","what":"Architectural Foundation","title":"Summary","text":"detailed State Field section, existing tools focus single databases different domains (genomics vs. pharmacology). dbparser’s architecture specifically designed multi-database pharmacological integration, building lessons learned evaluating alternatives. dvobject structure emerged need preserve complex relational hierarchies (drug → target → pathway → disease) providing consistent access patterns across heterogeneous sources. design enables downstream package ecosystem (dbdataset, covid19dbcand) published research applications technically prohibitive existing tools.","code":""},{"path":"https://docs.ropensci.org/dbparser/paper.html","id":"validation-through-ecosystem-development","dir":"","previous_headings":"","what":"Validation Through Ecosystem Development","title":"Summary","text":"extensibility dbparser’s architecture validated development two downstream packages build upon infrastructure: dbdataset [@dbdataset]: Provides pre-parsed DrugBank datasets ready--use R dataframe format, eliminating need users download parse large XML files. package leverages dbparser’s parsing functions create versioned, reproducible datasets machine learning exploratory analysis. covid19dbcand [@covid19dbcand]: Delivers curated COVID-19 drug candidate datasets extracted DrugBank pandemic response. package demonstrated dbparser’s value rapid response research, enabling researchers quickly access potential therapeutic candidates without time-consuming data extraction. downstream packages demonstrate dbparser’s dvobject structure parsing functions provide stable foundation building domain-specific data products—key indicator successful research software design.","code":""},{"path":[]},{"path":"https://docs.ropensci.org/dbparser/paper.html","id":"demonstrated-community-adoption-and-recognition","dir":"","previous_headings":"","what":"Demonstrated Community Adoption and Recognition","title":"Summary","text":"dbparser established essential infrastructure R pharmacoinformatics community since initial release 2019: Download Metrics: 50,000 cumulative downloads CRAN sustained adoption approximately 780 downloads per month, demonstrating consistent growth six years. Download trends show strong retention expanding user base across multiple continents. Community Recognition: Featured CRAN Epidemiology Task View, indicating recognition domain experts essential infrastructure epidemiological pharmacovigilance research. curated list represents packages deemed essential applied statistical work epidemiology, signaling package’s established role field. Code Quality Review: Achieves 98% test coverage earned OpenSSF Best Practices passing badge, placing top tier R research software. Successfully completed rigorous rOpenSci software peer review (Issue #347, February 2020), reviewers Hao Zhu Emma Mendelsohn providing substantial feedback improved API design, error handling, documentation comprehensiveness.","code":""},{"path":"https://docs.ropensci.org/dbparser/paper.html","id":"development-history-and-collaborative-engagement","dir":"","previous_headings":"","what":"Development History and Collaborative Engagement","title":"Summary","text":"package demonstrates sustained, collaborative development characteristic meaningful research software: Timeline: 6+ years active development (first commit: September 29, 2018; first CRAN release: January 2019) Commits: 614 commits demonstrating iterative refinement continuous improvement Contributors: 7 contributors spanning multiple institutions career stages User Diversity: Actively used researchers ranging Master’s students NIH scientists across multiple countries Issue Resolution: Responsive maintenance active engagement GitHub issues users diverse scientific backgrounds (academia, government, industry) Maintenance: Regular releases following semantic versioning (currently version 2.2.1, published January 8, 2026)","code":""},{"path":"https://docs.ropensci.org/dbparser/paper.html","id":"published-research-applications","dir":"","previous_headings":"","what":"Published Research Applications","title":"Summary","text":"dbparser enabled peer-reviewed research across multiple high-impact domains, demonstrating substantial realized impact: Drug Repurposing Studies: - Parolo et al. (2023) used dbparser Nature Scientific Reports single-cell-led drug repurposing Alzheimer’s disease research [@parolo2023single] - Pérez-Moraga et al. (2021) employed package Pharmaceutics COVID-19 drug repurposing using topological data analysis [@perez2021covid] - Schubert et al. (2022) applied dbparser Biomolecules transcriptome-guided identification drugs age-related hearing loss [@schubert2022transcriptome] Systems Biology Network Analysis: - Mercatelli et al. (2022) integrated dbparser SURFACER workflow published Briefings Bioinformatics (Oxford Academic) pan-cancer surface protein biomarker detection [@mercatelli2022detection] - Yang et al. (2021) utilized package research published Pharmacological Research mapping synthetic lethal interactions liver cancer [@yang2021mapping] - Su et al. (2024) incorporated dbparser multi-ancestry proteome-phenome-wide Mendelian randomization analysis medRxiv [@su2024multiancestry] Clinical Epidemiological Research: - Rischke et al. (2023) employed dbparser Nature Scientific Reports machine learning identification psoriatic arthritis activity signals [@rischke2023machine] - Namiot et al. (2023) used package Frontiers Pharmacology analyzing trends clinical trials International Clinical Trials Registry Platform [@namiot2023international] Software Integration Ecosystem Development: - Hammoud & Kramer (2020) integrated dbparser Multipath package published Biology (MDPI) generating reproducible pathway models [@hammoud2020multipath] - Hammoud et al. (2025) extended integration Multipath 2.0 published Computer Methods Programs Biomedicine (Elsevier) [@hammoud2025multipath2] body work—spanning Nature publications, Oxford Academic journals, domain-specific outlets—demonstrates dbparser actively enabling cutting-edge research drug discovery, systems pharmacology, machine learning applications, clinical epidemiology.","code":""},{"path":"https://docs.ropensci.org/dbparser/paper.html","id":"impact-beyond-citations","dir":"","previous_headings":"","what":"Impact Beyond Citations","title":"Summary","text":"package lowers technical barriers multi-database pharmacology research, transforming weeks custom parsing code minutes standardized workflow. democratization access particularly benefits: Early-career researchers lack extensive bioinformatics infrastructure Interdisciplinary teams requiring reproducible data pipelines Resource-limited institutions without dedicated computational support Educational contexts students learn computational pharmacology integration DrugBank modern pharmacovigilance databases (OnSIDES, TWOSIDES) enables analyses previously technically prohibitive, accelerating pace drug safety research repurposing studies.","code":""},{"path":"https://docs.ropensci.org/dbparser/paper.html","id":"downstream-package-ecosystem","dir":"","previous_headings":"","what":"Downstream Package Ecosystem","title":"Summary","text":"robustness dbparser’s design evidenced use foundational infrastructure additional R packages: dbdataset: Provides pre-parsed DrugBank datasets ready--analyze format, built entirely dbparser’s parsing infrastructure. 16 GitHub stars active maintenance, serves researchers need immediate access DrugBank data without local parsing. covid19dbcand: Created response COVID-19 pandemic, package delivered curated drug candidate datasets therapeutic research. demonstrated dbparser’s capability support rapid-response research public health emergencies, data extracted using dbparser version 1.2.0. packages maintain development histories, documentation, user bases relying dbparser stable infrastructure—hallmark sustainable research software enables innovation.","code":""},{"path":[]},{"path":"https://docs.ropensci.org/dbparser/paper.html","id":"core-parsing-architecture","dir":"","previous_headings":"","what":"Core Parsing Architecture","title":"Summary","text":"dbparser provides dedicated parsing functions supported database: Performance achieved streaming XML parsing via xml2 [@wickham2023xml2] high-speed CSV parsing via data.table::fread() [@dowle2023datatable]. Typical parsing times commodity hardware (8-core CPU, 16GB RAM): DrugBank full XML (~2.5GB) completes approximately 3-5 minutes; OnSIDES (~500MB total) parses 30 seconds; TWOSIDES (~1.2GB) completes approximately 1 minute.","code":""},{"path":"https://docs.ropensci.org/dbparser/paper.html","id":"example-workflow-anticoagulant-side-effect-analysis","dir":"","previous_headings":"","what":"Example Workflow: Anticoagulant Side Effect Analysis","title":"Summary","text":"analysis validates known clinical findings—hemorrhagic events represent primary safety concern anticoagulant therapy [@garcia2012anticoagulant]. integrated database enables researchers immediately cross-reference findings mechanistic target information DrugBank examine potential interaction effects TWOSIDES.","code":"library(dbparser) library(dplyr) # Parse and integrate databases drugbank_db <- parseDrugBank(\"drugbank_all_full_database.xml\") onsides_db <- parseOnSIDES(\"onsides_v2.0.0/\") # Chain merge operations for integrated analysis merged_db <- drugbank_db %>% merge_drugbank_onsides(onsides_db) # Identify anticoagulant drugs via therapeutic category anticoagulant_ids <- merged_db$drugbank$drugs$categories %>% filter(category == \"Anticoagulants\") %>% pull(drugbank_id) # Analyze side effect frequencies from integrated data side_effects <- merged_db$integrated_data$drugbank_onsides %>% filter(drugbank_id %in% anticoagulant_ids) %>% count(meddra_name, sort = TRUE) head(side_effects, 5) #> meddra_name frequency #> 1 Haemorrhage 847 #> 2 Anaemia 623 #> 3 Thrombocytopenia 412 #> 4 Ecchymosis 389 #> 5 Epistaxis 356"},{"path":"https://docs.ropensci.org/dbparser/paper.html","id":"ai-usage-disclosure","dir":"","previous_headings":"","what":"AI Usage Disclosure","title":"Summary","text":"Generative AI tools (Claude, Anthropic) used assist drafting portions manuscript, including reformatting bibliographic entries suggesting organizational structure. AI-generated content thoroughly reviewed, verified accuracy, substantially edited authors. core dbparser software implementation, architectural decisions, research contributions represent original human intellectual work developed six years (2018-2024) prior widespread availability modern generative AI coding assistants. Initial development majority codebase predate AI-assisted programming tools.","code":""},{"path":"https://docs.ropensci.org/dbparser/paper.html","id":"availability","dir":"","previous_headings":"","what":"Availability","title":"Summary","text":"dbparser available CRAN (install.packages(\"dbparser\")) development version hosted GitHub (https://github.com/ropensci/dbparser). Comprehensive documentation available https://docs.ropensci.org/dbparser/. package released MIT license. rOpenSci package, adheres strict code conduct. Community contributions, bug reports, feature requests welcomed GitHub issue tracker (https://github.com/ropensci/dbparser/issues).","code":""},{"path":"https://docs.ropensci.org/dbparser/paper.html","id":"acknowledgements","dir":"","previous_headings":"","what":"Acknowledgements","title":"Summary","text":"gratefully acknowledge creators maintainers DrugBank, OnSIDES TWOSIDES making invaluable data resources publicly available research community. thank rOpenSci community peer reviewers Hao Zhu Emma Mendelsohn constructive feedback software review process (ropensci/software-review#347) substantially improved package’s quality, documentation, API design. Special thanks Tatonetti Lab Columbia University (now Cedars-Sinai) developing maintaining OnSIDES, TWOSIDES, OFFSIDES resources. acknowledge contributors dbparser codebase users provided feedback, bug reports, feature suggestions past six years.","code":""},{"path":[]},{"path":"https://docs.ropensci.org/dbparser/reference/add_database_info.html","id":null,"dir":"Reference","previous_headings":"","what":"add_database_info Assign passed databases db metadata to passed dvobject — add_database_info","title":"add_database_info Assign passed databases db metadata to passed dvobject — add_database_info","text":"add_database_info Assign passed databases db metadata passed dvobject","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/add_database_info.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"add_database_info Assign passed databases db metadata to passed dvobject — add_database_info","text":"","code":"add_database_info( dvobject, db_type = \"DrugBank\", db_version = NULL, db_exported_date = NULL )"},{"path":"https://docs.ropensci.org/dbparser/reference/add_database_info.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"add_database_info Assign passed databases db metadata to passed dvobject — add_database_info","text":"dvobject dvobject assign metadata db_type database type (defualt=\"DrugBank\") db_version database version string db_exported_date database official export date","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/add_database_info.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"add_database_info Assign passed databases db metadata to passed dvobject — add_database_info","text":"dvobject","code":""},{"path":[]},{"path":"https://docs.ropensci.org/dbparser/reference/articles.html","id":null,"dir":"Reference","previous_headings":"","what":"Drugs/ Carriers/ Enzymes/ Targets/ Transporters articles element parser — articles","title":"Drugs/ Carriers/ Enzymes/ Targets/ Transporters articles element parser — articles","text":"Return list articles used references drugs carriers","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/articles.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drugs/ Carriers/ Enzymes/ Targets/ Transporters articles element parser — articles","text":"","code":"drugs_articles() carriers_articles() enzymes_articles() targets_articles() transporters_articles()"},{"path":"https://docs.ropensci.org/dbparser/reference/articles.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drugs/ Carriers/ Enzymes/ Targets/ Transporters articles element parser — articles","text":"tibble 4 variables: ref-id Identifier article referenced. unique across reference types (books, links, article, attachments). pubmed-id PubMed identifier article. citation Article citation standard format. parent_id drug/carrier/target/enzyme/transporter id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/attachments.html","id":null,"dir":"Reference","previous_headings":"","what":"Drugs/ Carriers/ Enzymes/ Targets/ Transporters attachments element parser — attachments","title":"Drugs/ Carriers/ Enzymes/ Targets/ Transporters attachments element parser — attachments","text":"Return list attachment used references drugs carriers","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/attachments.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drugs/ Carriers/ Enzymes/ Targets/ Transporters attachments element parser — attachments","text":"","code":"drugs_attachments() carriers_attachments() enzymes_attachments() targets_attachments() transporters_attachments()"},{"path":"https://docs.ropensci.org/dbparser/reference/attachments.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drugs/ Carriers/ Enzymes/ Targets/ Transporters attachments element parser — attachments","text":"tibble 4 variables: ref-id Identifier article referenced. unique across reference types (books, links, article, attachments). title title attachment. url url download attachment . parent_id drug/carrier/target/enzyme/transporter id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/books.html","id":null,"dir":"Reference","previous_headings":"","what":"Drugs/ Carriers/ Enzymes/ Targets/ Transporters books element parser — books","title":"Drugs/ Carriers/ Enzymes/ Targets/ Transporters books element parser — books","text":"Return list text books used references drugs, carriers, enzymes, targets transporters","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/books.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drugs/ Carriers/ Enzymes/ Targets/ Transporters books element parser — books","text":"","code":"drugs_textbooks() carriers_textbooks() enzymes_textbooks() targets_textbooks() transporters_textbooks()"},{"path":"https://docs.ropensci.org/dbparser/reference/books.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drugs/ Carriers/ Enzymes/ Targets/ Transporters books element parser — books","text":"tibble 4 variables: ref-id Identifier article referenced. unique across reference types (books, links, article, attachments). isbn ISBN identifying textbook. citation Textbook citation standard format. parent_id drug/ carrier/ target/ enzyme/ transporter id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/build_metadata.html","id":null,"dir":"Reference","previous_headings":"","what":"Build Metadata Data Frame — build_metadata","title":"Build Metadata Data Frame — build_metadata","text":"Build Metadata Data Frame","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/build_metadata.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Build Metadata Data Frame — build_metadata","text":"","code":"build_metadata(attrs, has_second_db, second_db_name)"},{"path":"https://docs.ropensci.org/dbparser/reference/build_metadata.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Build Metadata Data Frame — build_metadata","text":"attrs Attributes list has_second_db Logical indicating second database exists second_db_name Name second database attribute","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/build_metadata.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Build Metadata Data Frame — build_metadata","text":"Data.frame database metadata","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/cett_actions_doc.html","id":null,"dir":"Reference","previous_headings":"","what":"Carriers/ Enzymes/ Targets/ Transporters Actions parsers — cett_actions_doc","title":"Carriers/ Enzymes/ Targets/ Transporters Actions parsers — cett_actions_doc","text":"Collection related actions","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/cett_actions_doc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Carriers/ Enzymes/ Targets/ Transporters Actions parsers — cett_actions_doc","text":"","code":"carriers_actions() enzymes_actions() targets_actions() transporters_actions()"},{"path":"https://docs.ropensci.org/dbparser/reference/cett_actions_doc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Carriers/ Enzymes/ Targets/ Transporters Actions parsers — cett_actions_doc","text":"tibble 2 variables: action describe related action parent_id carrier/ target/ enzyme/ transporter id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/cett_doc.html","id":null,"dir":"Reference","previous_headings":"","what":"Carriers/ Enzymes/ Targets/ Transporters parsers — cett_doc","title":"Carriers/ Enzymes/ Targets/ Transporters parsers — cett_doc","text":"Protein targets drug action, enzymes inhibited/induced involved metabolism, carrier transporter proteins involved movement drug across biological membranes.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/cett_doc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Carriers/ Enzymes/ Targets/ Transporters parsers — cett_doc","text":"","code":"carriers() enzymes() targets() transporters()"},{"path":"https://docs.ropensci.org/dbparser/reference/cett_doc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Carriers/ Enzymes/ Targets/ Transporters parsers — cett_doc","text":"tibble 6 variables (8 enzymes): id Universal Protein Resource (UniProt) Identifier record name related name organism Organism protein comes . known_action Whether pharmacological action drug due target interaction. inhibition-strength Whether strength enzyme inhibition strong, moderate, unknown. applies enzymes induction-strength Whether strength enzyme induction strong unknown. applies enzymes position related position parent_id drugbank id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/cett_ex_identity_doc.html","id":null,"dir":"Reference","previous_headings":"","what":"Carriers/ Enzymes/ Targets/ Transporters Polypeptide External Identifiers parsers — cett_ex_identity_doc","title":"Carriers/ Enzymes/ Targets/ Transporters Polypeptide External Identifiers parsers — cett_ex_identity_doc","text":"Extract descriptions identified polypeptide external identifiers targets, enzymes, carriers, transporters.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/cett_ex_identity_doc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Carriers/ Enzymes/ Targets/ Transporters Polypeptide External Identifiers parsers — cett_ex_identity_doc","text":"","code":"carriers_polypep_ex_ident() enzymes_polypep_ex_ident() targets_polypep_ex_ident() transporters_polypep_ex_ident()"},{"path":"https://docs.ropensci.org/dbparser/reference/cett_ex_identity_doc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Carriers/ Enzymes/ Targets/ Transporters Polypeptide External Identifiers parsers — cett_ex_identity_doc","text":"tibble 3 variables: resource Name source database. identifier Identifier drug given resource. parent_key polypeptide id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/cett_go_doc.html","id":null,"dir":"Reference","previous_headings":"","what":"Carriers/ Enzymes/ Targets/ Transporters Polypeptide GO Classifier parsers — cett_go_doc","title":"Carriers/ Enzymes/ Targets/ Transporters Polypeptide GO Classifier parsers — cett_go_doc","text":"Extract descriptions identified polypeptide go classifier targets, enzymes, carriers, transporters.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/cett_go_doc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Carriers/ Enzymes/ Targets/ Transporters Polypeptide GO Classifier parsers — cett_go_doc","text":"","code":"carriers_polypeptides_go() enzymes_polypeptides_go() targets_polypeptides_go() transporters_polypeptides_go()"},{"path":"https://docs.ropensci.org/dbparser/reference/cett_go_doc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Carriers/ Enzymes/ Targets/ Transporters Polypeptide GO Classifier parsers — cett_go_doc","text":"tibble 3 variables: category description parent_key polypeptide id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/cett_nodes_options.html","id":null,"dir":"Reference","previous_headings":"","what":"returns carriers, enzymes,targets and transporters node valid options. — cett_nodes_options","title":"returns carriers, enzymes,targets and transporters node valid options. — cett_nodes_options","text":"returns carriers, enzymes,targets transporters node valid options.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/cett_nodes_options.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"returns carriers, enzymes,targets and transporters node valid options. — cett_nodes_options","text":"","code":"cett_nodes_options()"},{"path":"https://docs.ropensci.org/dbparser/reference/cett_nodes_options.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"returns carriers, enzymes,targets and transporters node valid options. — cett_nodes_options","text":"list CETT valid options","code":""},{"path":[]},{"path":"https://docs.ropensci.org/dbparser/reference/cett_poly_doc.html","id":null,"dir":"Reference","previous_headings":"","what":"Carriers/ Enzymes/ Targets/ Transporters Polypeptide parsers — cett_poly_doc","title":"Carriers/ Enzymes/ Targets/ Transporters Polypeptide parsers — cett_poly_doc","text":"Extract descriptions identified polypeptide targets, enzymes, carriers, transporters.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/cett_poly_doc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Carriers/ Enzymes/ Targets/ Transporters Polypeptide parsers — cett_poly_doc","text":"","code":"carriers_polypeptides() enzymes_polypeptides() targets_polypeptides() transporters_polypeptides()"},{"path":"https://docs.ropensci.org/dbparser/reference/cett_poly_doc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Carriers/ Enzymes/ Targets/ Transporters Polypeptide parsers — cett_poly_doc","text":"tibble 20 variables: id Universal Protein Resource (UniProt) identifier source Specifies whether identified polypeptide ID associated following UniProt knowledge bases: Swiss-Prot, manually annotated reviewed, TrEMBL, automatically annotated reviewed. name general_function General summary physiological function polypeptide specific_function specific description polypeptide’s physiological function within cell. gene_name short name commonly associated associated gene. Eg. PTGS1. locus specific chromosomal location position gene’s sequence chromosome. cellular_location cellular location polypeptide. transmembrane_regions Areas polypeptide sequence span biological membrane. signal_regions Location signal peptides within polypeptide sequence. theoretical_pi Theoretical isoelectric point. molecular_weight molecular weight polypeptide. chromosome_location chromosomal location polypeptide gene organism organism polypeptide functions. organism_ncbi_taxonomy_id amino_acid_sequence amino acid sequence polypeptide amino_acid_format gene_sequence sequence associated gene. gene_format parent_key carrier/ target/ enzyme/ transporter id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/cett_poly_pfms_doc.html","id":null,"dir":"Reference","previous_headings":"","what":"Carriers/ Enzymes/ Targets/ Transporters Polypeptide PFAMS parsers — cett_poly_pfms_doc","title":"Carriers/ Enzymes/ Targets/ Transporters Polypeptide PFAMS parsers — cett_poly_pfms_doc","text":"Extract descriptions identified polypeptide PFAMS targets, enzymes, carriers, transporters.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/cett_poly_pfms_doc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Carriers/ Enzymes/ Targets/ Transporters Polypeptide PFAMS parsers — cett_poly_pfms_doc","text":"","code":"carriers_polypeptides_pfams() enzymes_polypeptides_pfams() targets_polypeptides_pfams() transporters_polypeptides_pfams()"},{"path":"https://docs.ropensci.org/dbparser/reference/cett_poly_pfms_doc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Carriers/ Enzymes/ Targets/ Transporters Polypeptide PFAMS parsers — cett_poly_pfms_doc","text":"tibble 3 variables: name sequence associated gene. identifier parent_key polypeptide id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/cett_poly_syn_doc.html","id":null,"dir":"Reference","previous_headings":"","what":"Carriers/ Enzymes/ Targets/ Transporters Polypeptide Synonyms parsers — cett_poly_syn_doc","title":"Carriers/ Enzymes/ Targets/ Transporters Polypeptide Synonyms parsers — cett_poly_syn_doc","text":"Extract descriptions identified polypeptide synonyms targets, enzymes, carriers, transporters.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/cett_poly_syn_doc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Carriers/ Enzymes/ Targets/ Transporters Polypeptide Synonyms parsers — cett_poly_syn_doc","text":"","code":"carriers_polypeptides_syn() enzymes_polypeptides_syn() targets_polypeptides_syn() transporters_polypeptides_syn()"},{"path":"https://docs.ropensci.org/dbparser/reference/cett_poly_syn_doc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Carriers/ Enzymes/ Targets/ Transporters Polypeptide Synonyms parsers — cett_poly_syn_doc","text":"tibble 2 variables: synonym parent_key polypeptide id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/count_top_level_lists.html","id":null,"dir":"Reference","previous_headings":"","what":"Count Top Level Lists — count_top_level_lists","title":"Count Top Level Lists — count_top_level_lists","text":"Count Top Level Lists","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/count_top_level_lists.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count Top Level Lists — count_top_level_lists","text":"","code":"count_top_level_lists(obj)"},{"path":"https://docs.ropensci.org/dbparser/reference/count_top_level_lists.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count Top Level Lists — count_top_level_lists","text":"obj Object analyze","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/count_top_level_lists.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count Top Level Lists — count_top_level_lists","text":"Integer count top-level lists (excluding data.frames)","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/dbparser.html","id":null,"dir":"Reference","previous_headings":"","what":"dbparser: A package for reading and parsing DrugBank xml database. — dbparser","title":"dbparser: A package for reading and parsing DrugBank xml database. — dbparser","text":"main purpose `dbparser` package parse [DrugBank](https://go.drugbank.com/) database downloadable XML format [link](https://go.drugbank.com/releases/latest).","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/dbparser.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"dbparser: A package for reading and parsing DrugBank xml database. — dbparser","text":"parsed data can explored analyzed. achieve purpose, `dbparser“ package provides three main categories functions: - xml db reader, - DrugBank elements parsers, information kindly check reference/index (https://docs.ropensci.org/dbparser/reference/index.html)","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/dbparser.html","id":"xml-db-reader-functions","dir":"Reference","previous_headings":"","what":"xml db reader functions","title":"dbparser: A package for reading and parsing DrugBank xml database. — dbparser","text":"Reads DrugBank xml database build drug elements full tree memory","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/dbparser.html","id":"parsers-functions","dir":"Reference","previous_headings":"","what":"parsers functions","title":"dbparser: A package for reading and parsing DrugBank xml database. — dbparser","text":"parser function responsible parsing certain drug element returning tibble. Check tutorial (https://docs.ropensci.org/dbparser/articles/dbparser.html)","code":""},{"path":[]},{"path":"https://docs.ropensci.org/dbparser/reference/dbparser.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"dbparser: A package for reading and parsing DrugBank xml database. — dbparser","text":"Maintainer: Mohammed Ali moh_fcis@yahoo.com Authors: Ali Ezzat ali_ezzat85@yahoo.com contributors: Hao Zhu haozhu233@gmail.com [reviewer] Emma Mendelsohn [reviewer]","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_affected_organisms.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug Affected Organism parser — drug_affected_organisms","title":"Drug Affected Organism parser — drug_affected_organisms","text":"Organisms drug may display activity; activity may depend local susceptibility patterns resistance.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_affected_organisms.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug Affected Organism parser — drug_affected_organisms","text":"","code":"drug_affected_organisms()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_affected_organisms.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug Affected Organism parser — drug_affected_organisms","text":"tibble 2 variables: affected-organism affected-organism name drugbank_id drugbank id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_ahfs_codes.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug ahfs-codes parser — drug_ahfs_codes","title":"Drug ahfs-codes parser — drug_ahfs_codes","text":"American Hospital Formulary Service (AHFS) identifier drug.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_ahfs_codes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug ahfs-codes parser — drug_ahfs_codes","text":"","code":"drug_ahfs_codes()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_ahfs_codes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug ahfs-codes parser — drug_ahfs_codes","text":"tibble following variables: ahfs-code drugbank_id drugbank id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_atc_codes.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug ATC Codes element parser — drug_atc_codes","title":"Drug ATC Codes element parser — drug_atc_codes","text":"Anatomical Therapeutic Classification (ATC) code drug assigned World Health Organization Anatomical Chemical Classification System.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_atc_codes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug ATC Codes element parser — drug_atc_codes","text":"","code":"drug_atc_codes()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_atc_codes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug ATC Codes element parser — drug_atc_codes","text":"tibble 10 variables","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_atc_codes.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Drug ATC Codes element parser — drug_atc_codes","text":"`atc-code“ row one level. atc-code level> code code assigned World Health Organization Anatomical Therapeutic Chemical Classification system.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_calc_prop.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug Calculated Properties parser — drug_calc_prop","title":"Drug Calculated Properties parser — drug_calc_prop","text":"Drug properties predicted ChemAxon ALOGPS based imputed chemical structure. Associated links redirect descriptions specific term.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_calc_prop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug Calculated Properties parser — drug_calc_prop","text":"","code":"drug_calc_prop()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_calc_prop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug Calculated Properties parser — drug_calc_prop","text":"tibble 4 variables: kind Name property. value Predicted physicochemical properties; obtained use prediction software ALGOPS ChemAxon. source Name software used calculate property, either ChemAxon ALOGPS. drugbank_id drugbank id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_categories.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug Categories parser — drug_categories","title":"Drug Categories parser — drug_categories","text":"General categorizations drug.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_categories.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug Categories parser — drug_categories","text":"","code":"drug_categories()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_categories.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug Categories parser — drug_categories","text":"tibble 2 variables: category category name mesh-id Medical Subjects Headings (MeSH) identifier category. drugbank_id drugbank id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_classification.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug Classification parser — drug_classification","title":"Drug Classification parser — drug_classification","text":"description hierarchical chemical classification drug; imported ClassyFire.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_classification.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug Classification parser — drug_classification","text":"","code":"drug_classification()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_classification.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug Classification parser — drug_classification","text":"tibble 9 variables: description direct-parent kingdom superclass class subclass alternative-parent One alternative parents substituent One substituents drugbank_id drugbank id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_dosages.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug Dosages parser — drug_dosages","title":"Drug Dosages parser — drug_dosages","text":"list commercially available dosages drug.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_dosages.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug Dosages parser — drug_dosages","text":"","code":"drug_dosages()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_dosages.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug Dosages parser — drug_dosages","text":"tibble following variables: form pharmaceutical formulation drug introduced body route path drug product taken body. strength amount active drug ingredient provided dosage drugbank_id drugbank id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_exp_prop.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug Experimental Properties parser — drug_exp_prop","title":"Drug Experimental Properties parser — drug_exp_prop","text":"Drug properties experimentally proven","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_exp_prop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug Experimental Properties parser — drug_exp_prop","text":"","code":"drug_exp_prop()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_exp_prop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug Experimental Properties parser — drug_exp_prop","text":"tibble following variables: kind Name property. value Drug properties experimentally proven. source Reference source experimental data. drugbank_id drugbank id following experimental properties provided: Water Solubility experimentally determined aqueous solubility molecule. Molecular Formula Protein formula Biotech drugs Molecular Weight Protein weight Biotech drugs. Melting Point experimentally determined temperature drug molecule changes solid liquid atmospheric temperature. Boiling Point experimentally determined temperature drug molecule changes liquid gas atmospheric temperature. Hydrophobicity ability molecule repel water rather absorb dissolve water. Isoelectric Point pH value net electric charge molecule zero. caco2 Permeability continuous line heterogenous human epithelial colorectal adenocarcinoma cells, CAC02 cells employed model human intestinal absorption various drugs compounds. CAC02 cell permeability ultimately assay measure drug absorption. pKa experimentally determined pka value molecule logP experimentally determined partition coefficient (LogP) based ratio solubility molecule 1-octanol compared water. logS intrinsic solubility given compound concentration equilibrium solid phase dissolves solution, given natural logarithm (LogS) concentration. Radioactivity property spontaneously emit particles (alpha, beta, neutron) radiation (gamma, K capture), time, decay certain nuclides.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_external_links.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug External Links parser — drug_external_links","title":"Drug External Links parser — drug_external_links","text":"Links websites databases providing information drug.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_external_links.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug External Links parser — drug_external_links","text":"","code":"drug_external_links()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_external_links.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug External Links parser — drug_external_links","text":"tibble following variables: resource Name source website. identifier Identifier drug given resource drugbank_id drugbank id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_ex_identity.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug External Identifiers parser — drug_ex_identity","title":"Drug External Identifiers parser — drug_ex_identity","text":"Identifiers used websites databases providing information drug.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_ex_identity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug External Identifiers parser — drug_ex_identity","text":"","code":"drug_ex_identity()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_ex_identity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug External Identifiers parser — drug_ex_identity","text":"tibble following variables: resource Name source database. identifier Identifier drug given resource. drugbank_id drugbank id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_food_interactions.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug Groups parser — drug_food_interactions","title":"Drug Groups parser — drug_food_interactions","text":"Food may interact drug.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_food_interactions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug Groups parser — drug_food_interactions","text":"","code":"drug_food_interactions()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_food_interactions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug Groups parser — drug_food_interactions","text":"tibble following variables: food-interaction drugbank_id drugbank id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_general_information.html","id":null,"dir":"Reference","previous_headings":"","what":"Drugs General Information parser — drug_general_information","title":"Drugs General Information parser — drug_general_information","text":"description hierarchical chemical classification drug; imported ClassyFire.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_general_information.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drugs General Information parser — drug_general_information","text":"","code":"drug_general_information()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_general_information.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drugs General Information parser — drug_general_information","text":"tibble 15 variables: primary_key DrugBank id other_keys identifiers may associated drug type Either small molecule, biotech. Biotech used drug derived living systems organisms, usually composed high molecular weight mixtures protein, small molecule describes low molecular weight organic compound. name created Date drug first added DrugBank. updated Denotes drug last updated DrugBank. description Descriptions drug chemical properties, history regulatory status. cas_number Chemical Abstracts Service (CAS) registry number assigned drug. unii Unique Ingredient Identifier (UNII) drug. average_mass weighted average isotopic masses drug state One solid, liquid, gas monoisotopic_mass mass abundant isotope drug synthesis_reference Citation synthesis drug molecule. fda_label Contains URL accessing uploaded United States Food Drug Administration (FDA) Monograph drug. msds Contains URL accessing Material Safety Data Sheet (MSDS) drug.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_groups.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug Groups parser — drug_groups","title":"Drug Groups parser — drug_groups","text":"Groups drug belongs . May include : approved, vet_approved, nutraceutical, illicit, withdrawn, investigational, experimental.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_groups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug Groups parser — drug_groups","text":"","code":"drug_groups()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_groups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug Groups parser — drug_groups","text":"tibble 2 variables: group drugbank_id drugbank id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_interactions.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug Interactions parser — drug_interactions","title":"Drug Interactions parser — drug_interactions","text":"Drug-drug interactions detailing drugs , administered concomitantly drug interest, affect activity result adverse effects. interactions may synergistic antagonistic depending physiological effects mechanism action drug.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_interactions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug Interactions parser — drug_interactions","text":"","code":"drug_interactions()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_interactions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug Interactions parser — drug_interactions","text":"tibble following variables: drugbank-id DrugBank ID interacting drug. name Name interacting drug. description Textual description physiological consequences drug interaction drugbank_id parent drugbank id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_intern_brand.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug International Brands parser — drug_intern_brand","title":"Drug International Brands parser — drug_intern_brand","text":"proprietary names used manufacturers commercially available forms drug, focusing brand names products available countries Canada Unites States.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_intern_brand.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug International Brands parser — drug_intern_brand","text":"","code":"drug_intern_brand()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_intern_brand.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug International Brands parser — drug_intern_brand","text":"tibble 4 variables: brand proprietary, well-known name given drug manufacturer. company company manufacturer uses name. drugbank_id drugbank id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_manufacturers.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug Manufacturers parser — drug_manufacturers","title":"Drug Manufacturers parser — drug_manufacturers","text":"list companies manufacturing commercially available forms drug available Canada Unites States.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_manufacturers.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug Manufacturers parser — drug_manufacturers","text":"","code":"drug_manufacturers()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_manufacturers.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug Manufacturers parser — drug_manufacturers","text":"tibble following variables: generic list companies manufacturing generic form drug. url link companies manufacturing drug. drugbank_id drugbank id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_mixtures.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug Mixtures parser — drug_mixtures","title":"Drug Mixtures parser — drug_mixtures","text":"commercially available products drug available combination drug molecules","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_mixtures.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug Mixtures parser — drug_mixtures","text":"","code":"drug_mixtures()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_mixtures.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug Mixtures parser — drug_mixtures","text":"tibble 4 variables: name proprietary name provided manufacturer combination product. ingredients list ingredients, separated addition symbols supplemental-ingredients List additional active ingredients clinically relevant main indication product, separated addition symbols. drugbank_id drugbank id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_node_options.html","id":null,"dir":"Reference","previous_headings":"","what":"returns drug node valid options. — drug_node_options","title":"returns drug node valid options. — drug_node_options","text":"returns drug node valid options.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_node_options.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"returns drug node valid options. — drug_node_options","text":"","code":"drug_node_options()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_node_options.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"returns drug node valid options. — drug_node_options","text":"list drug valid options","code":""},{"path":[]},{"path":"https://docs.ropensci.org/dbparser/reference/drug_packagers.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug Packagers parser — drug_packagers","title":"Drug Packagers parser — drug_packagers","text":"list companies packaging drug re-distribution.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_packagers.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug Packagers parser — drug_packagers","text":"","code":"drug_packagers()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_packagers.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug Packagers parser — drug_packagers","text":"tibble 2 variables: name url link companies packaging drug re-distribution. drugbank_id drugbank id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_patents.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug Patents parser A property right issued by the United States Patent and Trademark Office (USPTO) to an inventor for a limited time, in exchange for public disclosure of the invention when the patent is granted. Drugs may be issued multiple patents. — drug_patents","title":"Drug Patents parser A property right issued by the United States Patent and Trademark Office (USPTO) to an inventor for a limited time, in exchange for public disclosure of the invention when the patent is granted. Drugs may be issued multiple patents. — drug_patents","text":"Drug Patents parser property right issued United States Patent Trademark Office (USPTO) inventor limited time, exchange public disclosure invention patent granted. Drugs may issued multiple patents.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_patents.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug Patents parser A property right issued by the United States Patent and Trademark Office (USPTO) to an inventor for a limited time, in exchange for public disclosure of the invention when the patent is granted. Drugs may be issued multiple patents. — drug_patents","text":"","code":"drug_patents()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_patents.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug Patents parser A property right issued by the United States Patent and Trademark Office (USPTO) to an inventor for a limited time, in exchange for public disclosure of the invention when the patent is granted. Drugs may be issued multiple patents. — drug_patents","text":"tibble following variables: number patent number(s) associated drug. country country issued patent rights. approved date patent request filed. expires date patent rights expire. pediatric-extension Indicates whether pediatric extension approved patent. Granted pediatric extensions provide additional 6 months market protection. drugbank_id drugbank id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_pathway.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug Pathway parser — drug_pathway","title":"Drug Pathway parser — drug_pathway","text":"Metabolic, disease, biological pathways drug involved , identified Small Molecule Protein Database (SMPDB).","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_pathway.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug Pathway parser — drug_pathway","text":"","code":"drug_pathway()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_pathway.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug Pathway parser — drug_pathway","text":"tibble following variables: smpdb-id Small Molecule Pathway Database identifier pathway. name Pathway name category Pathway category drugbank_id drugbank id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_pathway_drugs.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug Pathway Drugs parser — drug_pathway_drugs","title":"Drug Pathway Drugs parser — drug_pathway_drugs","text":"Drugs involved pathway.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_pathway_drugs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug Pathway Drugs parser — drug_pathway_drugs","text":"","code":"drug_pathway_drugs()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_pathway_drugs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug Pathway Drugs parser — drug_pathway_drugs","text":"tibble pathway drugs properties","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_pathway_enzyme.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug Pathway Enzymes parser — drug_pathway_enzyme","title":"Drug Pathway Enzymes parser — drug_pathway_enzyme","text":"Enzymes involved pathway.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_pathway_enzyme.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug Pathway Enzymes parser — drug_pathway_enzyme","text":"","code":"drug_pathway_enzyme()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_pathway_enzyme.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug Pathway Enzymes parser — drug_pathway_enzyme","text":"tibble pathway properties","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_pdb_entries.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug pdb-entries parser — drug_pdb_entries","title":"Drug pdb-entries parser — drug_pdb_entries","text":"Protein Data Bank (PDB) identifiers drug.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_pdb_entries.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug pdb-entries parser — drug_pdb_entries","text":"","code":"drug_pdb_entries()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_pdb_entries.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug pdb-entries parser — drug_pdb_entries","text":"tibble following variables: pdb-entry drugbank_id drugbank id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_pharmacology.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug Pharmacology parser — drug_pharmacology","title":"Drug Pharmacology parser — drug_pharmacology","text":"Describes use, mechanism action, pharmacokinetics, pharmacodynamics, physiological biochemical effects body.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_pharmacology.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug Pharmacology parser — drug_pharmacology","text":"","code":"drug_pharmacology()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_pharmacology.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug Pharmacology parser — drug_pharmacology","text":"tibble following variables: indication approved conditions, diseases, states drug can safely effectively used. indication considered FDA-approved following designations: NDA, ANDA, BLA, OTC. May also include indications countries, Canada (Health Canada) Europe (European Medicines Agency). pharmacodynamics description drug modifies affects organism used . May include effects body desired (enzyme protein targets example) undesired (also known “side effects”). contrast pharmacokinetics, describes body modifies drug used. mechanism_of_action component pharmacodynamics describes biochemical interaction drug produces intended effect. May include exact molecular protein enzyme targets / description physiological effects produced. toxicity adverse reaction, side effect, may may occur use drug. May attributed number effects including: enhanced therapeutic effect, rare anaphylactic reactions, interactions medications, unanticipated binding molecule different sites within body. metabolism description chemical degradation drug molecule within body; commonly enzymes Cytochrome P450 (CYP) system liver. absorption description movement drug site administration bloodstream target tissue. Common pharmacokinetic metrics used evaluate absorption include Area Curve (AUC), bioavailability (F), maximum concentration (Cmax), time maximum concentration (Tmax). half-life period time takes amount drug body reduced one half. Provides description quickly drug eliminated much available bloodstream. protein-binding description drug’s affinity plama proteins proportion drug bound circulation within body. route_of_elimination description pathway used excrete drug body. Common pharmacokinetic parameters used evaluate excretion include elimination half life, renal clearance, tracking radiolabelled compounds renal GI system. volume_of_distribution Vd drug represents degree distributed body tissue compared plasma. clearance pharmacokinetic measurement rate removal drug plasma, expressed mL/min; reflects rate elimination drug. drugbank_id drugbank id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_prices.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug Prices Parsers — drug_prices","title":"Drug Prices Parsers — drug_prices","text":"Unit drug prices","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_prices.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug Prices Parsers — drug_prices","text":"","code":"drug_prices()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_prices.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug Prices Parsers — drug_prices","text":"tibble 5 variables: description cost Drug price per unit currency Currency price, example: US. unit parent_id drugbank id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_products.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug Products parser — drug_products","title":"Drug Products parser — drug_products","text":"list commercially available products Canada United States contain drug.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_products.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug Products parser — drug_products","text":"","code":"drug_products()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_products.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug Products parser — drug_products","text":"tibble 32 variables: name proprietary name(s) provided manufacturer commercially available products containing drug. labeller corporation responsible labelling product. ndc-id National Drug Code (NDC) identifier drug ndc-product-code National Drug Code (NDC) product code FDA National Drug Code directory. dpd-id Drug Product Database (DPD) identification number (.k.. DIN) Canadian Drug Product Database. present drugs marketed Canada ema-product-code EMA product code European Medicines Agency Database. present products authorized central procedure marketing European Union. ema-ma-number EMA marketing authorization number European Medicines Agency Database. present products authorized central procedure marketing European Union. started-marketing-starting date market approval. ended-marketing-ending date market approval. dosage-form pharmaceutical formulation drug introduced body. strength amount active drug ingredient provided dosage route path drug product taken body fda-application-number New Drug Application [NDA] number assigned drug FDA. --counter list Counter (OTC) forms drug. generic Whether product generic drug. approved Indicates whether drug approved regulating government. country country commercially available drug approved. source Source product information. example, value DPD indicates information retrieved Canadian Drug Product Database. standing One good, discordant, deprecated. Distinguishes products date ingredient information (good) products conflicting information (discordant) products removed active label (deprecated). standing-updated-date standing last updated standing-reason Explains non-good standing product. One : ingredient_change, code_duplication, invalid, removed. jurisdiction-marketing-category marketing category product jurisdiction branded Whether product named brand prescription Whether product available prescription unapproved Whether product approved jurisdiction vaccine Whether product vaccine allergenic Whether product used allergenic testing cosmetic Whether product cosmetic, sunscreen kit Whether product kit composed multiple distinct parts solo Whether product single active ingredient available Whether product can sold jurisdiction drugbank_id drugbank id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_reactions.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug Reactions Parsers — drug_reactions","title":"Drug Reactions Parsers — drug_reactions","text":"Extract sequential representation metabolic reactions drug molecule involved . Depending available information, may include metabolizing enzymes, reaction type, substrates, products, pharmacological activity metabolites, structural representation biochemical reactions.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_reactions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug Reactions Parsers — drug_reactions","text":"","code":"drug_reactions()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_reactions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug Reactions Parsers — drug_reactions","text":"tibble 5 variables: sequence Reactions displayed within numerical sequence left_drugbank_name substrate reaction. Maybe drug metabolite. rightt_drugbank_name product reaction. Maybe drug metabolite. left_drugbank_id right_drugbank_id parent_id drugbank id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_reactions_enzymes.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug Reactions Enzymes Parsers — drug_reactions_enzymes","title":"Drug Reactions Enzymes Parsers — drug_reactions_enzymes","text":"Enzymes involved metabolizing drug","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_reactions_enzymes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug Reactions Enzymes Parsers — drug_reactions_enzymes","text":"","code":"drug_reactions_enzymes()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_reactions_enzymes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug Reactions Enzymes Parsers — drug_reactions_enzymes","text":"tibble 3 variables: name uniprot-id parent_id drugbank id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_salts.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug Salts parser — drug_salts","title":"Drug Salts parser — drug_salts","text":"Available salt forms drug. Ions hydrochloride, sodium, sulfate often added drug molecule increase solubility, dissolution, absorption.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_salts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug Salts parser — drug_salts","text":"","code":"drug_salts()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_salts.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug Salts parser — drug_salts","text":"tibble 1 variables: drugbank-id DrugBank identifiers available salt form(s). name Name available salt form(s) unii Unique Ingredient Identifier (UNII) available salt form(s). cas-number Chemical Abstracts Service (CAS) registry number assigned salt form(s) drug. inchikey IUPAC International Chemical Identifier (InChi) key identifier available salt form(s). average-mass Average molecular mass: weighted average isotopic masses salt. monoisotopic-mass mass abundant isotope salt smiles simplified molecular-input line-entry system (SMILES) line notation used describing structure chemical species using short ASCII strings; calculated ChemAxon. inchi prediction IUPAC International Chemical Identifier (InChI); imported ChemAxon. formula Indicates simple numbers type atom within molecule; calculated ChemAxon. drugbank_id parent drugbank id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_sequences.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug Sequences parser — drug_sequences","title":"Drug Sequences parser — drug_sequences","text":"amino acid sequence; provided drug peptide.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_sequences.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug Sequences parser — drug_sequences","text":"","code":"drug_sequences()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_sequences.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug Sequences parser — drug_sequences","text":"tibble following variables: sequence textual representation sequence format Currently, FASTA format used drugbank_id drugbank id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_sequences.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Drug Sequences parser — drug_sequences","text":"Describes peptide sequences biotech drugs","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_snp_adverse_reactions.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug SNP Adverse Drug Reactions parser — drug_snp_adverse_reactions","title":"Drug SNP Adverse Drug Reactions parser — drug_snp_adverse_reactions","text":"adverse drug reactions may occur result listed single nucleotide polymorphisms (SNPs)","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_snp_adverse_reactions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug SNP Adverse Drug Reactions parser — drug_snp_adverse_reactions","text":"","code":"drug_snp_adverse_reactions()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_snp_adverse_reactions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug SNP Adverse Drug Reactions parser — drug_snp_adverse_reactions","text":"tibble following variables: protein-name Proteins involved SNP. gene-symbol Genes involved SNP. uniprot-id Universal Protein Resource (UniProt) identifiers proteins involved pathway. rs-id SNP Database identifier single nucleotide polymorphism. allele alleles associated identified SNP. adverse-reaction description pubmed-id Reference PubMed article. drugbank_id drugbank id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_snp_effects.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug SNP Effects parser — drug_snp_effects","title":"Drug SNP Effects parser — drug_snp_effects","text":"list single nucleotide polymorphisms (SNPs) relevant drug activity metabolism, effects may pharmacological activity. SNP effects patient may require close monitoring, increase ","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_snp_effects.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug SNP Effects parser — drug_snp_effects","text":"","code":"drug_snp_effects()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_snp_effects.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug SNP Effects parser — drug_snp_effects","text":"tibble following variables: protein-name Proteins involved SNP. gene-symbol Genes involved SNP. uniprot-id Universal Protein Resource (UniProt) identifiers proteins involved pathway. rs-id SNP Database identifier single nucleotide polymorphism. allele alleles associated identified SNP. defining-change description written description SNP effects. pubmed-id Reference PubMed article. drugbank_id drugbank id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_syn.html","id":null,"dir":"Reference","previous_headings":"","what":"Drug Synonyms parser — drug_syn","title":"Drug Synonyms parser — drug_syn","text":"names identifiers associated drug.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/drug_syn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drug Synonyms parser — drug_syn","text":"","code":"drug_syn()"},{"path":"https://docs.ropensci.org/dbparser/reference/drug_syn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drug Synonyms parser — drug_syn","text":"tibble 3 variables: language Names drug languages English. coder Organization source providing synonym. example, INN indicates synonym International Nonproprietary Name, IUPAC indicates synonym nomenclature designated International Union Pure Applied Chemistry. drugbank_id drugbank id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/find_dataframes_recursive.html","id":null,"dir":"Reference","previous_headings":"","what":"Find All Data Frames Recursively — find_dataframes_recursive","title":"Find All Data Frames Recursively — find_dataframes_recursive","text":"Find Data Frames Recursively","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/find_dataframes_recursive.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Find All Data Frames Recursively — find_dataframes_recursive","text":"","code":"find_dataframes_recursive(x, prefix = \"\")"},{"path":"https://docs.ropensci.org/dbparser/reference/find_dataframes_recursive.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Find All Data Frames Recursively — find_dataframes_recursive","text":"x Object search prefix Current path prefix","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/find_dataframes_recursive.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Find All Data Frames Recursively — find_dataframes_recursive","text":"Data.frame paths, dimensions, sizes","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/find_second_database.html","id":null,"dir":"Reference","previous_headings":"","what":"Find Second Database in Attributes — find_second_database","title":"Find Second Database in Attributes — find_second_database","text":"Find Second Database Attributes","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/find_second_database.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Find Second Database in Attributes — find_second_database","text":"","code":"find_second_database(attrs)"},{"path":"https://docs.ropensci.org/dbparser/reference/find_second_database.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Find Second Database in Attributes — find_second_database","text":"attrs Attributes list","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/find_second_database.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Find Second Database in Attributes — find_second_database","text":"List has_second_db (logical) second_db_name (character)","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/init_dvobject.html","id":null,"dir":"Reference","previous_headings":"","what":"init_dvobject initialize dvobject — init_dvobject","title":"init_dvobject initialize dvobject — init_dvobject","text":"init_dvobject initialize dvobject","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/init_dvobject.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"init_dvobject initialize dvobject — init_dvobject","text":"","code":"init_dvobject()"},{"path":"https://docs.ropensci.org/dbparser/reference/init_dvobject.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"init_dvobject initialize dvobject — init_dvobject","text":"dvobject","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/links.html","id":null,"dir":"Reference","previous_headings":"","what":"Drugs/ Carriers/ Enzymes/ Targets/ Transporters links element parser — links","title":"Drugs/ Carriers/ Enzymes/ Targets/ Transporters links element parser — links","text":"Return list websites used references Drugs/ Carriers/ Enzymes/ Targets/ Transporters","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/links.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drugs/ Carriers/ Enzymes/ Targets/ Transporters links element parser — links","text":"","code":"drugs_links() carriers_links() enzymes_links() targets_links() transporters_links()"},{"path":"https://docs.ropensci.org/dbparser/reference/links.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drugs/ Carriers/ Enzymes/ Targets/ Transporters links element parser — links","text":"tibble 4 variables: ref-id Name source website title Identifier drug given resource url url website parent_id drug/ carrier/ target/ enzyme/ transporter id","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/merge_drugbank_onsides.html","id":null,"dir":"Reference","previous_headings":"","what":"Merge DrugBank and OnSIDES Database Objects — merge_drugbank_onsides","title":"Merge DrugBank and OnSIDES Database Objects — merge_drugbank_onsides","text":"Creates integrated dvobject object linking DrugBank dvobject OnSIDES dvobject using RxNorm CUIs bridge.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/merge_drugbank_onsides.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Merge DrugBank and OnSIDES Database Objects — merge_drugbank_onsides","text":"","code":"merge_drugbank_onsides(db_object, onsides_db)"},{"path":"https://docs.ropensci.org/dbparser/reference/merge_drugbank_onsides.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Merge DrugBank and OnSIDES Database Objects — merge_drugbank_onsides","text":"db_object dvobject `parseDrugBank()` existing merged dvobject (containing `$drugbank`). onsides_db dvobject produced `dbparser::parseOnSIDES()`.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/merge_drugbank_onsides.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Merge DrugBank and OnSIDES Database Objects — merge_drugbank_onsides","text":"new dvobject containing integrated data.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/merge_drugbank_onsides.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Merge DrugBank and OnSIDES Database Objects — merge_drugbank_onsides","text":"function performs following key steps: 1. Creates mapping table DrugBank IDs RxNorm CUIs DrugBank object. 2. Enriches relevant OnSIDES tables (`vocab_rxnorm_ingredient` optionally `high_confidence`) adding `drugbank_id` column. 3. Assembles new list object containing original tables plus enriched ones ID mapping table . resulting object allows powerful queries span mechanistic data DrugBank clinical side-effect data OnSIDES. Supports piping chaining merge functions.","code":""},{"path":[]},{"path":"https://docs.ropensci.org/dbparser/reference/merge_drugbank_onsides.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Merge DrugBank and OnSIDES Database Objects — merge_drugbank_onsides","text":"","code":"if (FALSE) { # \\dontrun{ # First, parse the individual databases drugbank <- parseDrugBank(\"path/to/drugbank.xml\") onsides <- parseOnSIDES(\"path/to/onsides_csvs/\") # Now, merge them into a single, powerful object merged_db <- merge_drugbank_onsides(drugbank, onsides) # --- Example Analysis: Find the protein targets of all drugs known to --- # --- cause the side effect \"Hepatitis\" with high confidence. --- # 1. Find the MedDRA ID for \"Hepatitis\" hepatitis_id <- merged_db$onsides$vocab_meddra_adverse_effect %>% filter(meddra_name == \"Hepatitis\") %>% pull(meddra_id) # 2. Find all drug ingredients linked to this effect in the high_confidence table drug_ids_causing_hepatitis <- merged_db$onsides$high_confidence_enriched %>% filter(effect_meddra_id == hepatitis_id) %>% pull(drugbank_id) %>% na.omit() %>% unique() # 3. Look up the targets for these DrugBank IDs targets_of_interest <- merged_db$targets %>% filter(parent_key %in% drug_ids_causing_hepatitis) %>% select(drug_id = parent_key, target_name = name, gene_name) head(targets_of_interest) } # }"},{"path":"https://docs.ropensci.org/dbparser/reference/merge_drugbank_twosides.html","id":null,"dir":"Reference","previous_headings":"","what":"Merge a DrugBank dvobject with a TWOSIDES dvobject — merge_drugbank_twosides","title":"Merge a DrugBank dvobject with a TWOSIDES dvobject — merge_drugbank_twosides","text":"Integrates drug-drug interaction data TWOSIDES rich mechanistic information DrugBank. function chainable can accept raw DrugBank object already-merged dvobject.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/merge_drugbank_twosides.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Merge a DrugBank dvobject with a TWOSIDES dvobject — merge_drugbank_twosides","text":"","code":"merge_drugbank_twosides(db_object, twosides_db)"},{"path":"https://docs.ropensci.org/dbparser/reference/merge_drugbank_twosides.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Merge a DrugBank dvobject with a TWOSIDES dvobject — merge_drugbank_twosides","text":"db_object dvobject `parseDrugBank()` existing merged dvobject. twosides_db dvobject `parseTWOSIDES()`.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/merge_drugbank_twosides.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Merge a DrugBank dvobject with a TWOSIDES dvobject — merge_drugbank_twosides","text":"new, nested dvobject TWOSIDES data added.","code":""},{"path":[]},{"path":"https://docs.ropensci.org/dbparser/reference/parseDrugBank.html","id":null,"dir":"Reference","previous_headings":"","what":"parseDrugBank — parseDrugBank","title":"parseDrugBank — parseDrugBank","text":"parses given DrugBank XML database dvobject. dvobject list data.frames data.frame represents part parsed data (.e drugs, prices, carriers, ...)","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/parseDrugBank.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"parseDrugBank — parseDrugBank","text":"","code":"parseDrugBank( db_path, drug_options = NULL, parse_salts = FALSE, parse_products = FALSE, references_options = NULL, cett_options = NULL )"},{"path":"https://docs.ropensci.org/dbparser/reference/parseDrugBank.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"parseDrugBank — parseDrugBank","text":"db_path string, full path DrugBank xml zip file. drug_options character vector, list sub drug related nodes names options parse (default = NULL). Check drug_node_options() available options. value `NULL` `drug_general_information` placed returned dvobject. parse_salts boolean, parse salts info (default = FALSE) parse_products boolean, parse products info (default = FALSE) references_options character vector, list sub references related nodes names options parse (default = NULL). Check references_node_options() available options. cett_options character vector, list sub cett related nodes names options parse (default = NULL). Check cett_nodes_options() available options.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/parseDrugBank.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"parseDrugBank — parseDrugBank","text":"dvobject","code":""},{"path":[]},{"path":"https://docs.ropensci.org/dbparser/reference/parseOnSIDES.html","id":null,"dir":"Reference","previous_headings":"","what":"Parse the OnSIDES Core Relational Database — parseOnSIDES","title":"Parse the OnSIDES Core Relational Database — parseOnSIDES","text":"Parses core relational tables OnSIDES database.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/parseOnSIDES.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Parse the OnSIDES Core Relational Database — parseOnSIDES","text":"","code":"parseOnSIDES( dataDir, include_high_confidence = TRUE, db_version = NULL, db_exported_date = NULL )"},{"path":"https://docs.ropensci.org/dbparser/reference/parseOnSIDES.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Parse the OnSIDES Core Relational Database — parseOnSIDES","text":"dataDir string specifying path directory containing OnSIDES CSV files. include_high_confidence Logical. TRUE (default), function also parse `high_confidence.csv` file, pre-aggregated summary ingredient--effect relationships. file found, warning issued. db_version used onside version (default = NULL) db_exported_date used onside release date (default = NULL)","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/parseOnSIDES.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Parse the OnSIDES Core Relational Database — parseOnSIDES","text":"dvobject","code":""},{"path":[]},{"path":"https://docs.ropensci.org/dbparser/reference/parseTWOSIDES.html","id":null,"dir":"Reference","previous_headings":"","what":"Parse the TWOSIDES Drug-Drug Interaction Database — parseTWOSIDES","title":"Parse the TWOSIDES Drug-Drug Interaction Database — parseTWOSIDES","text":"Reads TWOSIDES data file, contains adverse event data pairs drugs taken concurrently (N=2 interactions).","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/parseTWOSIDES.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Parse the TWOSIDES Drug-Drug Interaction Database — parseTWOSIDES","text":"","code":"parseTWOSIDES(twosides_file_path, db_version = NULL, db_exported_date = NULL)"},{"path":"https://docs.ropensci.org/dbparser/reference/parseTWOSIDES.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Parse the TWOSIDES Drug-Drug Interaction Database — parseTWOSIDES","text":"twosides_file_path Path TWOSIDES data file (e.g., 'TWOSIDES.csv.gz'). db_version used twoside version (default = NULL) db_exported_date used twoside release date (default = NULL)","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/parseTWOSIDES.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Parse the TWOSIDES Drug-Drug Interaction Database — parseTWOSIDES","text":"dvobject class `TWOSIDESDB` containing `drug_drug_interactions` data frame associated metadata.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/parseTWOSIDES.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Parse the TWOSIDES Drug-Drug Interaction Database — parseTWOSIDES","text":"TWOSIDES database drug-drug interaction safety signals mined FDA's Adverse Event Reporting System using approach used generate OffSIDES. Database fields follow: drug_1_rxnorn_id RxNORM identifier drug 1 drug_1_concept_name RxNORM name string drug 1 drug_2_rxnorm_id RxNORM identifier drug 2 drug_2_concept_name RxNORM name string drug 3 condition_meddra_id MedDRA identifier side effect condition_concpet_name MedDRA name string side effect number reports pair drugs report side effect B number reports pair drugs report side effect C number reports PSM matched drugs (including perhaps single versions drug 1 drug 2) report side effect D number reports PSM matched drugs side effects PRR Proportional reporting ratio, PRR=(/(+B))/(C/(C+D)) PRR_error Error estimate PRR mean_reporting_frequency Proportion reports drug report side effect, /(+B)","code":""},{"path":[]},{"path":"https://docs.ropensci.org/dbparser/reference/parse_cett_node.html","id":null,"dir":"Reference","previous_headings":"","what":"Run all CETT related parsers — parse_cett_node","title":"Run all CETT related parsers — parse_cett_node","text":"Run parsers retrieve carriers, enzymes, targets transporters related information","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/parse_cett_node.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Run all CETT related parsers — parse_cett_node","text":"","code":"parse_cett_node(cett_options)"},{"path":"https://docs.ropensci.org/dbparser/reference/parse_cett_node.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Run all CETT related parsers — parse_cett_node","text":"list drugs parsed tibbles","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/parse_drug_nodes.html","id":null,"dir":"Reference","previous_headings":"","what":"Run all drug related parsers — parse_drug_nodes","title":"Run all drug related parsers — parse_drug_nodes","text":"Run parsers retrieve drugs related information","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/parse_drug_nodes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Run all drug related parsers — parse_drug_nodes","text":"","code":"parse_drug_nodes(drug_options)"},{"path":"https://docs.ropensci.org/dbparser/reference/parse_drug_nodes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Run all drug related parsers — parse_drug_nodes","text":"drug_options - list options parse","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/parse_drug_nodes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Run all drug related parsers — parse_drug_nodes","text":"list drugs parsed tibbles","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/parse_references_node.html","id":null,"dir":"Reference","previous_headings":"","what":"Drugs/ Carriers/ Enzymes/ Targets/ Transporters references element parser — parse_references_node","title":"Drugs/ Carriers/ Enzymes/ Targets/ Transporters references element parser — parse_references_node","text":"Drugs/ Carriers/ Enzymes/ Targets/ Transporters references element parser","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/parse_references_node.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drugs/ Carriers/ Enzymes/ Targets/ Transporters references element parser — parse_references_node","text":"","code":"parse_references_node(references_options)"},{"path":"https://docs.ropensci.org/dbparser/reference/parse_references_node.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drugs/ Carriers/ Enzymes/ Targets/ Transporters references element parser — parse_references_node","text":"list references drugs, carriers, enzymes, targets transporters","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/read_drugbank_xml_db.html","id":null,"dir":"Reference","previous_headings":"","what":"Reads DrugBank xml database and load it into memory. — read_drugbank_xml_db","title":"Reads DrugBank xml database and load it into memory. — read_drugbank_xml_db","text":"read_drugbank_xml_db loads DrugBank xml database full tree memory.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/read_drugbank_xml_db.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reads DrugBank xml database and load it into memory. — read_drugbank_xml_db","text":"","code":"read_drugbank_xml_db(db_path)"},{"path":"https://docs.ropensci.org/dbparser/reference/read_drugbank_xml_db.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reads DrugBank xml database and load it into memory. — read_drugbank_xml_db","text":"db_path string, full path DrugBank xml zip file.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/read_drugbank_xml_db.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reads DrugBank xml database and load it into memory. — read_drugbank_xml_db","text":"loaded DB NULL","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/read_drugbank_xml_db.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Reads DrugBank xml database and load it into memory. — read_drugbank_xml_db","text":"functions reads DrugBank xml database load memory later processing. Hence; method must called function package needs called one time . takes one single mandatory argument location DrugBank db.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/references_node_options.html","id":null,"dir":"Reference","previous_headings":"","what":"returns references node valid options. — references_node_options","title":"returns references node valid options. — references_node_options","text":"returns references node valid options.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/references_node_options.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"returns references node valid options. — references_node_options","text":"","code":"references_node_options()"},{"path":"https://docs.ropensci.org/dbparser/reference/references_node_options.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"returns references node valid options. — references_node_options","text":"list references valid options","code":""},{"path":[]},{"path":"https://docs.ropensci.org/dbparser/reference/show_dvobject_metadata.html","id":null,"dir":"Reference","previous_headings":"","what":"Display dvobject Metadata — show_dvobject_metadata","title":"Display dvobject Metadata — show_dvobject_metadata","text":"Displays information passed dbobject object including basic info, database metadata, data.frames contained within nested lists.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/show_dvobject_metadata.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Display dvobject Metadata — show_dvobject_metadata","text":"","code":"show_dvobject_metadata(obj, return_df = FALSE)"},{"path":"https://docs.ropensci.org/dbparser/reference/show_dvobject_metadata.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Display dvobject Metadata — show_dvobject_metadata","text":"obj dvobject return_df Logical. TRUE, returns metadata data.frame without printing. Default FALSE.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/show_dvobject_metadata.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Display dvobject Metadata — show_dvobject_metadata","text":"Invisibly returns data.frame containing dvobject metadata","code":""},{"path":[]},{"path":"https://docs.ropensci.org/dbparser/reference/show_dvobject_metadata.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Display dvobject Metadata — show_dvobject_metadata","text":"","code":"if (FALSE) { # \\dontrun{ display_merged_db_attrs(drugbank) metadata <- display_merged_db_attrs(drugbank, return_df = TRUE) } # }"},{"path":"https://docs.ropensci.org/dbparser/reference/subset_drugbank_dvobject.html","id":null,"dir":"Reference","previous_headings":"","what":"Subset a DrugBank dvobject by a vector of DrugBank IDs — subset_drugbank_dvobject","title":"Subset a DrugBank dvobject by a vector of DrugBank IDs — subset_drugbank_dvobject","text":"Subset DrugBank dvobject vector DrugBank IDs","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/subset_drugbank_dvobject.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Subset a DrugBank dvobject by a vector of DrugBank IDs — subset_drugbank_dvobject","text":"","code":"subset_drugbank_dvobject(dvobject, drug_ids)"},{"path":"https://docs.ropensci.org/dbparser/reference/subset_drugbank_dvobject.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Subset a DrugBank dvobject by a vector of DrugBank IDs — subset_drugbank_dvobject","text":"dvobject dvobject `parseDrugBank()`. drug_ids character vector `drugbank_id` values keep.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/subset_drugbank_dvobject.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Subset a DrugBank dvobject by a vector of DrugBank IDs — subset_drugbank_dvobject","text":"new, smaller dvobject structure attributes.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/subset_drugbank_dvobject.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Subset a DrugBank dvobject by a vector of DrugBank IDs — subset_drugbank_dvobject","text":"Intelligently filters DrugBank dvobject retain data associated specified list drugbank_ids. correctly handles deep, multi-level nested structure entire object, including complex relationships within `cett` list.","code":""},{"path":[]},{"path":"https://docs.ropensci.org/dbparser/reference/subset_drugbank_dvobject.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Subset a DrugBank dvobject by a vector of DrugBank IDs — subset_drugbank_dvobject","text":"","code":"if (FALSE) { # \\dontrun{ library(dbparser) one_drug <- subset_drugbank_dvobject(dvobject = dbdataset::drugbank, drug_ids = \"DB00001\") } # }"},{"path":"https://docs.ropensci.org/dbparser/reference/subset_onsides_dvobject.html","id":null,"dir":"Reference","previous_headings":"","what":"Subset an OnSIDES dvobject by a vector of RxNorm Ingredient IDs (Schema-Aware) — subset_onsides_dvobject","title":"Subset an OnSIDES dvobject by a vector of RxNorm Ingredient IDs (Schema-Aware) — subset_onsides_dvobject","text":"Intelligently filters OnSIDES dvobject cascading filters relational tables, ensuring final subset self-consistent.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/subset_onsides_dvobject.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Subset an OnSIDES dvobject by a vector of RxNorm Ingredient IDs (Schema-Aware) — subset_onsides_dvobject","text":"","code":"subset_onsides_dvobject(dvobject, ingredient_ids)"},{"path":"https://docs.ropensci.org/dbparser/reference/subset_onsides_dvobject.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Subset an OnSIDES dvobject by a vector of RxNorm Ingredient IDs (Schema-Aware) — subset_onsides_dvobject","text":"dvobject dvobject `parseOnSIDES()`. ingredient_ids character vector RxNorm CUIs (ingredients) keep.","code":""},{"path":"https://docs.ropensci.org/dbparser/reference/subset_onsides_dvobject.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Subset an OnSIDES dvobject by a vector of RxNorm Ingredient IDs (Schema-Aware) — subset_onsides_dvobject","text":"new, smaller dvobject structure.","code":""},{"path":[]},{"path":[]},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"dbparser-221","dir":"Changelog","previous_headings":"","what":"dbparser 2.2.1","title":"dbparser 2.2.1","text":"CRAN release: 2026-01-08 major feature release focused expanding dbparser’s capabilities real-world pharmacovigilance drug-drug interaction analysis. integration engine formalized around “Hub Spoke” model, DrugBank acting central hub.","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"new-features-2-2-1","dir":"Changelog","previous_headings":"","what":"NEW FEATURES","title":"dbparser 2.2.1","text":"Parses relational CSV files OnSIDES database, modern resource adverse drug events extracted FDA labels. Returns dvobject containing 7 core relational tables optional high_confidence summary table. Parses TWOSIDES database, leading resource drug-drug interaction (DDI) adverse event signals real-world data. Returns dvobject containing drug_drug_interactions table. parser correctly handles known column name misspellings source data (e.g., drug_1_rxnorn_id). Merges DrugBank dvobject OnSIDES dvobject. Automatically creates enriched integrated_data list, linking OnSIDES data DrugBank IDs via RxNorm CUIs. function chainable, meaning can used %>% pipeline merge functions. Merges DrugBank dvobject TWOSIDES dvobject. Performs “double join” enrich drugs interaction pair DrugBank IDs names. Uses robust “union” logic keep interactions even one two drugs present input DrugBank object. function also chainable. Intelligently filters DrugBank dvobject retain data associated specified list drugbank_ids. correctly handles deep, multi-level nested structure entire object, including complex relationships within cett list. Intelligently filters OnSIDES dvobject cascading filters relational tables, ensuring final subset self-consistent. Adding metadata existing dvobject objects using function: add_database_info() Displays information passed dbobject object including basic info, database metadata, data.frames contained within nested lists. Run vignette(\"dbparser_2_2\", package = \"dbparser\") info","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"documentation-2-2-1","dir":"Changelog","previous_headings":"","what":"DOCUMENTATION","title":"dbparser 2.2.1","text":"comprehensive new tutorial demonstrating full, three-way integration DrugBank, OnSIDES, TWOSIDES. Includes complete scientific case study analyzing single-drug vs. polypharmacy risks. Introduces reproducible example workflow using pre-computed RDS data object hosted externally keep package lightweight. Run vignette(\"drugbank_nside\", package = \"dbparser\") info Updated existing vignette package Readme enhanced examples","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"bug-fixes--minor-improvements-2-2-1","dir":"Changelog","previous_headings":"","what":"BUG FIXES & MINOR IMPROVEMENTS","title":"dbparser 2.2.1","text":"Several minor fixes done","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"dbparser-203","dir":"Changelog","previous_headings":"","what":"dbparser 2.0.3","title":"dbparser 2.0.3","text":"CRAN release: 2024-04-20","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"enhancements-2-0-3","dir":"Changelog","previous_headings":"","what":"Enhancements","title":"dbparser 2.0.3","text":"Updated unit tests latest DrugBank DB info v 5.1.12","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"bugs-fixes-2-0-3","dir":"Changelog","previous_headings":"","what":"Bugs Fixes","title":"dbparser 2.0.3","text":"Fixed “Drug Targets two drugbank_id columns” bug (#163)","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"dbparser-202","dir":"Changelog","previous_headings":"","what":"dbparser 2.0.2","title":"dbparser 2.0.2","text":"CRAN release: 2024-02-16","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"enhancements-2-0-2","dir":"Changelog","previous_headings":"","what":"Enhancements","title":"dbparser 2.0.2","text":"Replaced “-” “_” tibbles column names (#111) Renamed “primary_key” “drugbank_id” parsed drugs “general_information” tibble (#111) Replaced “parent_key” keywords different tibbles column names original parent name (#111) Updated Vignette use interactive canvasXpress package plots","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"dbparser-201","dir":"Changelog","previous_headings":"","what":"dbparser 2.0.1","title":"dbparser 2.0.1","text":"CRAN release: 2023-03-27","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"bugs-fixes-2-0-1","dir":"Changelog","previous_headings":"","what":"Bugs Fixes","title":"dbparser 2.0.1","text":"Fixed output references (#147) Updated parseDrugBanKparameters default values (#146) Fixed package documentation references (#144) Fixed CRAN error linux info (#145)","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"dbparser-200","dir":"Changelog","previous_headings":"","what":"dbparser 2.0.0","title":"dbparser 2.0.0","text":"CRAN release: 2023-03-17","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"breaking-changes-2-0-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"dbparser 2.0.0","text":"Deprecated saving parsed data given database (#140) Deprecated saving parsed data csv file (#140) Deprecated old structure public methods (#141) Updated minimum R required version 3.5 (#143)","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"new-features-2-0-0","dir":"Changelog","previous_headings":"","what":"New features","title":"dbparser 2.0.0","text":"Introduced new methods paring DrugBank DB returning dvobject (#141)","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"major-updates-2-0-0","dir":"Changelog","previous_headings":"","what":"Major Updates","title":"dbparser 2.0.0","text":"Updated unit tests work new data structure (#141)","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"minor-fixes-2-0-0","dir":"Changelog","previous_headings":"","what":"Minor Fixes","title":"dbparser 2.0.0","text":"Removed RMariaDB dependency (#129) Fix pkgdown configuration reference (#136)","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"dbparser-120","dir":"Changelog","previous_headings":"","what":"dbparser 1.2.0","title":"dbparser 1.2.0","text":"CRAN release: 2020-08-26","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"ui-changes-1-2-0","dir":"Changelog","previous_headings":"","what":"UI Changes","title":"dbparser 1.2.0","text":"Introduce progress bar parser functions","code":""},{"path":[]},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"collective-parsers-1-2-0","dir":"Changelog","previous_headings":"New Parsers","what":"Collective Parsers","title":"dbparser 1.2.0","text":"drugs, cett References Parsers","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"elements-parsers-1-2-0","dir":"Changelog","previous_headings":"New Parsers","what":"Elements Parsers","title":"dbparser 1.2.0","text":"attachments parsers drugs CETT drug_pharmacology parser Rename drugs_books parser drugs_textbooks Rename drug_all parser run_all_parsers Rename drug parser drug_general_information","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"documentation-update-1-2-0","dir":"Changelog","previous_headings":"","what":"Documentation Update:","title":"dbparser 1.2.0","text":"Add returned parsed data structure Explain returned data functionality whole elements Point related/similar parsers","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"package-design-1-2-0","dir":"Changelog","previous_headings":"","what":"Package design","title":"dbparser 1.2.0","text":"thinking contribute dbparser, now parsers implemented R6 classes.","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"minor-fixes-1-2-0","dir":"Changelog","previous_headings":"","what":"Minor Fixes","title":"dbparser 1.2.0","text":"Update database saver functions accommodate new DrugBank data size.","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"dbparser-112","dir":"Changelog","previous_headings":"","what":"dbparser 1.1.2","title":"dbparser 1.1.2","text":"CRAN release: 2020-06-08","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"major-changes-1-1-2","dir":"Changelog","previous_headings":"","what":"Major Changes","title":"dbparser 1.1.2","text":"Enhance many memory performance issues many parsers. Change drug classification representations extract useful information. ### Minor Changes Change drug tibbles features names ### DEFUNCT Size columns drugs main table longer exist, full statistical analysis later using dvminer package.","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"dbparser-111","dir":"Changelog","previous_headings":"","what":"dbparser 1.1.1","title":"dbparser 1.1.1","text":"CRAN release: 2020-05-10 Fix column size issue importing SQL Server (#91) Fix dbparser upcoming CRAN release dplyr issues (#92) Fix CRAN Notes (#93) Fix package documentation site references","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"dbparser-110","dir":"Changelog","previous_headings":"","what":"dbparser 1.1.0","title":"dbparser 1.1.0","text":"CRAN release: 2020-02-21","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"major-changes-1-1-0","dir":"Changelog","previous_headings":"","what":"Major Changes","title":"dbparser 1.1.0","text":"Functions splitted 6 categories DrugBank Database Loading, Carriers, Targets, Transporters, Drug common parsers. function names changed reflect function family. related documentation also updated (#66, #75). dbparser now can cite package calling citation(\"dbparser\") (#71). Adding user friendly error messages (#76, #81). User can now pass DBI database connection parser functions argument beside SQLite MariaDB (#87).","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"defunct-1-1-0","dir":"Changelog","previous_headings":"","what":"DEFUNCT","title":"dbparser 1.1.0","text":"open_db, open_mdb close_db functions longer supported. Creating maintaining database completely user responsibility database connection can passed parser functions (#87).","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"documentation-fixes-1-1-0","dir":"Changelog","previous_headings":"","what":"DOCUMENTATION FIXES","title":"dbparser 1.1.0","text":"New tutorials use dbparser created (#78, #79). Contribution guide added. Code conduct added (#70). Enhance function reference documentation include section type (#68).","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"dbparser-104","dir":"Changelog","previous_headings":"","what":"dbparser 1.0.4","title":"dbparser 1.0.4","text":"CRAN release: 2019-08-28 Fix save drugs tibbles csv several issues. Update SQL database tibbles saver functions. Update SQL database saver functions documentations. Support MariaDB introduce related functionalities.","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"dbparser-103","dir":"Changelog","previous_headings":"","what":"dbparser 1.0.3","title":"dbparser 1.0.3","text":"CRAN release: 2019-07-11 Fix CRAN errors notes","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"dbparser-102","dir":"Changelog","previous_headings":"","what":"dbparser 1.0.2","title":"dbparser 1.0.2","text":"CRAN release: 2019-07-08 Fix zip file location issue Replace Secondary third keys columns drug framework other_keys column contains keys might exist addition primary key Add average-mass, monoisotopic-mass calculated-properties parsers. Support saving parsed drugs related parsed database csv","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"dbparser-101","dir":"Changelog","previous_headings":"","what":"dbparser 1.0.1","title":"dbparser 1.0.1","text":"CRAN release: 2019-04-16 Fix CRAN Note Improve documentation Refactor unused functions Remove Count features drug data set Fix several typos documentation code Fix consistency issue CLASS tibbles Returned dbparser Check DrugBank database exist parsing Add support international_brands salts elements Properly rename features clear names Reduce datasets size getting unique rows Support reading zip file containing DrugBank xml database","code":""},{"path":"https://docs.ropensci.org/dbparser/news/index.html","id":"dbparser-100","dir":"Changelog","previous_headings":"","what":"dbparser 1.0.0","title":"dbparser 1.0.0","text":"CRAN release: 2018-12-17 Initial release contains core functionalities","code":""}] diff --git a/docs/sitemap.xml b/docs/sitemap.xml index 395348e9..4ddc9175 100644 --- a/docs/sitemap.xml +++ b/docs/sitemap.xml @@ -9,6 +9,7 @@ https://docs.ropensci.org/dbparser/CODE_OF_CONDUCT.html https://docs.ropensci.org/dbparser/CONTRIBUTING.html https://docs.ropensci.org/dbparser/index.html +https://docs.ropensci.org/dbparser/Intersterllar_Next.html https://docs.ropensci.org/dbparser/LICENSE-text.html https://docs.ropensci.org/dbparser/news/index.html https://docs.ropensci.org/dbparser/paper.html From ca7186566027a0cf1b6bf24ce9a41b74b9272b89 Mon Sep 17 00:00:00 2001 From: Mohammed Ali Date: Sat, 14 Feb 2026 11:46:16 +0200 Subject: [PATCH 10/10] - Updated Readme --- README.Rmd | 4 ++++ README.md | 3 +++ 2 files changed, 7 insertions(+) diff --git a/README.Rmd b/README.Rmd index c59dfd6c..b8dac3eb 100644 --- a/README.Rmd +++ b/README.Rmd @@ -24,6 +24,10 @@ library(ggplot2) [![Rdoc Documentation](https://img.shields.io/badge/Doc-Rdoc-blue.svg)](https://www.rdocumentation.org/packages/dbparser) [![CII Best Practices](https://bestpractices.coreinfrastructure.org/projects/3311/badge)](https://bestpractices.coreinfrastructure.org/projects/3311) [![rOpenSci Peer-Reviewed](https://badges.ropensci.org/347_status.svg)](https://github.com/ropensci/software-review/issues/347) +[![JOSS Paper](https://joss.theoj.org/papers/3212f2fb07013b8fb1cec499bb9e8381/status.svg)](https://joss.theoj.org/papers/3212f2fb07013b8fb1cec499bb9e8381) +[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.18608628.svg)](https://doi.org/10.5281/zenodo.18608628) + + ## Overview Drugs databases vary too much in their formats and structures which making related diff --git a/README.md b/README.md index 2ef050cc..c4ab1e82 100644 --- a/README.md +++ b/README.md @@ -18,6 +18,9 @@ Documentation](https://img.shields.io/badge/Doc-Rdoc-blue.svg)](https://www.rdoc Practices](https://bestpractices.coreinfrastructure.org/projects/3311/badge)](https://bestpractices.coreinfrastructure.org/projects/3311) [![rOpenSci Peer-Reviewed](https://badges.ropensci.org/347_status.svg)](https://github.com/ropensci/software-review/issues/347) +[![JOSS +Paper](https://joss.theoj.org/papers/3212f2fb07013b8fb1cec499bb9e8381/status.svg)](https://joss.theoj.org/papers/3212f2fb07013b8fb1cec499bb9e8381) +[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.18608628.svg)](https://doi.org/10.5281/zenodo.18608628) ## Overview