From ec3b29e2725d74ef0c289a00a86f46df54b36c1a Mon Sep 17 00:00:00 2001 From: Mohammed Ali Date: Sat, 14 Feb 2026 16:57:55 +0200 Subject: [PATCH] - updated Readme --- README.Rmd | 275 +++++++++++++++++++++++----------------- README.md | 314 +++++++++++++++++++++++----------------------- codemeta.json | 4 +- docs/index.html | 279 +++++++++++++++++++++++++++-------------- docs/index.md | 319 ++++++++++++++++++++++++----------------------- docs/llms.txt | 319 ++++++++++++++++++++++++----------------------- docs/pkgdown.yml | 2 +- docs/search.json | 2 +- 8 files changed, 832 insertions(+), 682 deletions(-) diff --git a/README.Rmd b/README.Rmd index b8dac3eb..b4ebf362 100644 --- a/README.Rmd +++ b/README.Rmd @@ -2,7 +2,7 @@ output: github_document --- - + ```{r setup, include = FALSE} knitr::opts_chunk$set( @@ -11,117 +11,105 @@ knitr::opts_chunk$set( fig.path = "man/figures/README-", out.width = "100%" ) -library(dplyr) -library(ggplot2) ``` + # dbparser -[![CRAN\_Status\_Badge](http://www.r-pkg.org/badges/version/dbparser)](https://cran.r-project.org/package=dbparser) +[![CRAN_Status_Badge](http://www.r-pkg.org/badges/version/dbparser)](https://cran.r-project.org/package=dbparser) +[![JOSS Paper](https://joss.theoj.org/papers/3212f2fb07013b8fb1cec499bb9e8381/status.svg)](https://joss.theoj.org/papers/3212f2fb07013b8fb1cec499bb9e8381) +[![rOpenSci Peer-Reviewed](https://badges.ropensci.org/347_status.svg)](https://github.com/ropensci/software-review/issues/347) +[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.18608628.svg)](https://doi.org/10.5281/zenodo.18608628) [![codecov](https://codecov.io/gh/ropensci/dbparser/branch/master/graph/badge.svg)](https://app.codecov.io/gh/ropensci/dbparser) -[![Project Status: Active – The project has reached a stable, usable state and is being actively developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active) -[![Lifecycle: stable](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://lifecycle.r-lib.org/articles/stages.html) [![metacran downloads](https://cranlogs.r-pkg.org/badges/grand-total/dbparser)](https://cran.r-project.org/package=dbparser) -[![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) - - +[![Lifecycle: stable](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://lifecycle.r-lib.org/articles/stages.html) ## Overview -Drugs databases vary too much in their formats and structures which making related -data analysis not a very easy job and requires a lot of efforts to work on only -two databases together such as [DrugBank](https://go.drugbank.com/), [OnSIDES](https://onsidesdb.org/), and [TWOSIDES](https://tatonettilab.org/resources/nsides/). -Hence, `dbparser` package aims to parse different public drugs databases into a -single and unified format R object called `dvobject` (stands for drugverse object). +`dbparser` is an [rOpenSci](https://ropensci.org/) peer-reviewed R package +that parses and integrates major pharmacological databases into standardized, +analysis-ready R objects called `dvobject`s (drugverse objects). -With recent updates, `dbparser` has evolved into an **integration engine**, allowing you to merge mechanistic data (DrugBank) with real-world phenotypic data (OnSIDES) and drug-drug interaction risks (TWOSIDES). +Pharmacological databases use incompatible formats and structures, forcing +researchers to write custom parsing scripts — a process that consumes +60–80% of analysis time. `dbparser` eliminates this bottleneck with +unified parsing functions, chainable merge operations, and a consistent +output structure that enables reproducible, cross-database analyses. -That should help in: +With recent updates, `dbparser` has evolved into an **integration engine**, +allowing you to merge mechanistic data (DrugBank) with real-world phenotypic +data (OnSIDES) and drug-drug interaction risks (TWOSIDES). -- working with single data object and not multiple databases in different formats, -- using R analysis capabilities easily on drugs data, -- 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. +## Installation -## dbparser in Advanced Research +```r +# From CRAN (stable) +install.packages("dbparser") -dbparser serves as data infrastructure for cutting-edge research: +# From GitHub (development) +# install.packages("pak") +pak::pak("ropensci/dbparser") +``` -- **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 +## Supported Databases -## dvobject Structure -`dvobject` introduces a unified and compressed format of drugs data. -It is an R list object. +### DrugBank (The Mechanistic Hub) -**For a single database (e.g., DrugBank):** -It contains one or more of the following sub-lists: +[DrugBank](https://go.drugbank.com/) is a comprehensive database +containing detailed drug, pharmacological, and target information. +As both a bioinformatics and a cheminformatics resource, DrugBank +combines detailed drug data (chemical, pharmacological, pharmaceutical) +with comprehensive drug target information (sequence, structure, pathway). +More information can be found [here](https://go.drugbank.com/about). -- **drugs**: list of data.frames that contain drugs information (i.e. synonyms, classifications, ...) and it is the only mandatory list -- **salts**: data.frame contains drugs salts information -- **products**: data.frame of commercially available drugs products in the world -- **references**: data.frame of articles, links and textbooks about drugs or CETT data -- **cett**: list of data.frames contain targets, enzymes, carriers and transporters information +- **Parser:** `parseDrugBank()` +- **Input:** Full XML database ([download](https://go.drugbank.com/releases/latest) — + requires free [account](https://go.drugbank.com/public_users/sign_up), may take a couple of days) +- **Tested versions:** 5.1.0 through 5.1.12 +- **Alternative:** Use [dbdataset](https://interstellar-egypt.github.io/dbdataset/) + for pre-parsed data without downloading the XML (GitHub only, exceeds CRAN size limit) +- **Tutorial:** [DrugBank Parsing Vignette](https://docs.ropensci.org/dbparser/articles/dbparser.html) -**For a merged database (Integrated Pharmacovigilance):** -When databases are merged using `merge_drugbank_onsides` or `merge_drugbank_twosides`, the `dvobject` becomes a nested structure containing: - -- **drugbank**: The mechanistic hub. -- **onsides**: The side-effect data (from FDA labels). -- **twosides**: The drug-drug interaction data. -- **integrated_data**: Enriched tables that bridge the databases (e.g., linking DrugBank IDs to OnSIDES adverse events). -- **metadata**: Detailed provenance for all contained datasets. - -## Drug Databases -Parsers are available for the following databases (it is in progress list) - -### 1. DrugBank - -[DrugBank](https://go.drugbank.com/) database is a comprehensive, freely -accessible, online database containing information on drugs and drug -targets. As both a bioinformatics and a cheminformatics resource, -DrugBank combines detailed drug (i.e. chemical, pharmacological and -pharmaceutical) data with comprehensive drug target (i.e. sequence, -structure, and pathway) information. More information about DrugBank can -be found [here](https://go.drugbank.com/about). - -In its raw form, the DrugBank database is a single -XML file. Users must create an [account](https://go.drugbank.com/public_users/sign_up) -with DrugBank and request permission to [download](https://go.drugbank.com/releases/latest) -the database. Note that this may take a couple of days. - -The `dbparser` package parses the DrugBank XML database into `R` tibbles that can be explored and analyzed by the user, check [this tutorial](https://docs.ropensci.org/dbparser/articles/dbparser.html) for more details. - -If you are waiting for access to the DrugBank database, or do not intend to do a deep dive with -the data, you may wish to use the `dbdataset` -[package](https://interstellar-egypt.github.io/dbdataset/), which contains -the DrugBank database already parsed into `dvobject`. Note that this is a large package that -exceeds the limit set by CRAN. It is only available on GitHub. - -`dbparser` is tested against DrugBank versions *5.1.0* through *5.1.12* -successfully. If you find errors with these versions or any other -version please submit an issue +If you find errors with any DrugBank version, please submit an issue [here](https://github.com/ropensci/dbparser/issues). -### 2. OnSIDES (The Phenotype) -[OnSIDES](https://onsidesdb.org/) provides adverse drug events extracted from thousands of FDA drug labels using machine learning. -* **Parser:** `parseOnSIDES()` -* **Input:** Directory containing OnSIDES CSV files. +### OnSIDES (Adverse Drug Events) + +[OnSIDES](https://onsidesdb.org/) provides adverse drug events +extracted from thousands of FDA drug labels using machine learning. + +- **Parser:** `parseOnSIDES()` +- **Input:** Directory containing OnSIDES CSV files + +### TWOSIDES (Drug-Drug Interactions) + +[TWOSIDES](https://tatonettilab.org/resources/nsides/) provides data +on adverse events arising when two drugs are taken together. + +- **Parser:** `parseTWOSIDES()` +- **Input:** `TWOSIDES.csv.gz` file + +## Quick Start + +### Parse a Single Database + +```r +library(dbparser) + +# Parse DrugBank +drugbank_db <- parseDrugBank("data/drugbank.xml") + +# Parse OnSIDES +onsides_db <- parseOnSIDES("data/onsides/") -### 3. TWOSIDES (Polypharmacy) -[TWOSIDES](https://tatonettilab.org/resources/nsides/) provides data on drug-drug interactions and the adverse events that arise when two drugs are taken together. -* **Parser:** `parseTWOSIDES()` -* **Input:** The `TWOSIDES.csv.gz` file. +# Parse TWOSIDES +twosides_db <- parseTWOSIDES("data/TWOSIDES.csv.gz") +``` -## Quick Start: Integration Pipeline +### Integration Pipeline -The power of `dbparser` lies in its ability to chain parsers and mergers together. Here is how you can build a complete pharmacovigilance dataset: +The power of `dbparser` lies in its ability to chain parsers and mergers +together. Here is how you can build a complete pharmacovigilance dataset: ```r library(dbparser) @@ -133,47 +121,102 @@ 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) %>% +# DrugBank serves as the hub. 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) ``` -For a detailed case study, please refer to the [Integrated Pharmacovigilance Vignette](https://docs.ropensci.org/dbparser/articles/drugbank_nside.html). +For a detailed case study, see the +[Integrated Pharmacovigilance Vignette](https://docs.ropensci.org/dbparser/articles/drugbank_nside.html). -## Installation +## The dvobject Structure -You can install the released version of dbparser from -[CRAN](https://CRAN.R-project.org) with: +`dvobject` is a unified, compressed format for pharmacological data — +an R list object that preserves complex relational hierarchies while +enabling consistent access patterns. -``` r -install.packages("dbparser") -``` -or you can install the latest updates directly from the repo +**For a single database (e.g., DrugBank):** -``` r -library(devtools) -devtools::install_github("ropensci/dbparser") -``` +- **drugs**: list of data frames containing drug information (synonyms, classifications, etc.) — the only mandatory component +- **salts**: data frame of drug salt information +- **products**: data frame of commercially available drug products worldwide +- **references**: data frame of articles, links, and textbooks about drugs or CETT data +- **cett**: list of data frames containing targets, enzymes, carriers, and transporters information + +**For a merged database (Integrated Pharmacovigilance):** + +When databases are merged using `merge_drugbank_onsides` or +`merge_drugbank_twosides`, the `dvobject` becomes a nested structure: + +- **drugbank**: The mechanistic hub +- **onsides**: Side-effect data (from FDA labels) +- **twosides**: Drug-drug interaction data +- **integrated_data**: Enriched tables bridging databases (e.g., linking DrugBank IDs to OnSIDES adverse events) +- **metadata**: Detailed provenance for all contained datasets + +## Research Impact + +`dbparser` has enabled **10+ peer-reviewed publications** in leading journals: + +| Domain | Journal | Reference | +|--------|---------|-----------| +| Alzheimer's Drug Repurposing | *Nature Scientific Reports* | Parolo et al. (2023) | +| COVID-19 Therapeutics | *Pharmaceutics* | Pérez-Moraga et al. (2021) | +| Pan-Cancer Biomarkers | *Briefings in Bioinformatics* | Mercatelli et al. (2022) | +| Pathway Modeling | *Computer Methods and Programs in Biomedicine* | Hammoud et al. (2025) | +| Clinical Trial Analysis | *Frontiers in Pharmacology* | Namiot et al. (2023) | -## Code of Conduct -Please note that the 'dbparser' project is released with a - [Contributor Code of Conduct](https://docs.ropensci.org/dbparser/CODE_OF_CONDUCT.html). - By contributing to this project, you agree to abide by its terms. - -## Contributing Guide -👍🎉 First off, thanks for taking the time to contribute! 🎉👍 -Please review our [Contributing Guide](https://docs.ropensci.org/dbparser/CONTRIBUTING.html). +📊 **50,000+ CRAN downloads** | Featured in the +[CRAN Epidemiology Task View](https://cran.r-project.org/web/views/Epidemiology.html) -## Share the love ❤️ +For the full list, see our +[JOSS paper](https://joss.theoj.org/papers/3212f2fb07013b8fb1cec499bb9e8381). -Think **dbparser** is useful? Let others discover it, by telling them in person, via Twitter or a blog post. +## Ecosystem -Using **dbparser** for a paper you are writing? Consider citing it -```{r} +| Package | Description | Links | +|---------|-------------|-------| +| [dbdataset](https://interstellar-egypt.github.io/dbdataset/) | Pre-parsed DrugBank datasets ready for analysis | [GitHub](https://github.com/interstellar-egypt/dbdataset) | +| [covid19dbcand](https://github.com/interstellar-egypt/covid19dbcand) | COVID-19 drug candidate datasets | [GitHub](https://github.com/interstellar-egypt/covid19dbcand) | +| [periscope2](https://periscopeapps.org/) | Shiny framework for interactive dashboards | [CRAN](https://cran.r-project.org/package=periscope2) | + +## Citation + +If you use `dbparser` in published research, please cite our JOSS paper: + +``` +Ali et al., (2026). dbparser: An R Package for Parsing and Integrating +Pharmacological Databases. Journal of Open Source Software, 11(118), +9950, https://doi.org/10.21105/joss.09950 +``` + +```r citation("dbparser") ``` + +If you find `dbparser` useful, consider ⭐ starring the +[GitHub repository](https://github.com/ropensci/dbparser) and +sharing it with colleagues. + +## Enterprise Support + +For custom database integrations, enterprise support, training, +or deployment assistance — `dbparser` is maintained by +[Interstellar Consultation Services](https://interstellar-egypt.com). + +📧 info@interstellar-egypt.com + +## Contributing + +We welcome contributions! Please review our +[Contributing Guide](https://docs.ropensci.org/dbparser/CONTRIBUTING.html). + +Please note that the `dbparser` project is released with a +[Contributor Code of Conduct](https://docs.ropensci.org/dbparser/CODE_OF_CONDUCT.html). +By contributing to this project, you agree to abide by its terms. + +[![ropensci_footer](https://ropensci.org/public_images/github_footer.png)](https://ropensci.org) diff --git a/README.md b/README.md index c4ab1e82..75de9022 100644 --- a/README.md +++ b/README.md @@ -1,148 +1,113 @@ - + # dbparser [![CRAN_Status_Badge](http://www.r-pkg.org/badges/version/dbparser)](https://cran.r-project.org/package=dbparser) +[![JOSS +Paper](https://joss.theoj.org/papers/3212f2fb07013b8fb1cec499bb9e8381/status.svg)](https://joss.theoj.org/papers/3212f2fb07013b8fb1cec499bb9e8381) +[![rOpenSci +Peer-Reviewed](https://badges.ropensci.org/347_status.svg)](https://github.com/ropensci/software-review/issues/347) +[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.18608628.svg)](https://doi.org/10.5281/zenodo.18608628) [![codecov](https://codecov.io/gh/ropensci/dbparser/branch/master/graph/badge.svg)](https://app.codecov.io/gh/ropensci/dbparser) -[![Project Status: Active – The project has reached a stable, usable -state and is being actively -developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active) -[![Lifecycle: -stable](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://lifecycle.r-lib.org/articles/stages.html) [![metacran downloads](https://cranlogs.r-pkg.org/badges/grand-total/dbparser)](https://cran.r-project.org/package=dbparser) -[![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) +[![Lifecycle: +stable](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://lifecycle.r-lib.org/articles/stages.html) ## Overview -Drugs databases vary too much in their formats and structures which -making related data analysis not a very easy job and requires a lot of -efforts to work on only two databases together such as -[DrugBank](https://go.drugbank.com/), [OnSIDES](https://onsidesdb.org/), -and [TWOSIDES](https://tatonettilab.org/resources/nsides/). +`dbparser` is an [rOpenSci](https://ropensci.org/) peer-reviewed R +package that parses and integrates major pharmacological databases into +standardized, analysis-ready R objects called `dvobject`s (drugverse +objects). -Hence, `dbparser` package aims to parse different public drugs databases -into a single and unified format R object called `dvobject` (stands for -drugverse object). +Pharmacological databases use incompatible formats and structures, +forcing researchers to write custom parsing scripts — a process that +consumes 60–80% of analysis time. `dbparser` eliminates this bottleneck +with unified parsing functions, chainable merge operations, and a +consistent output structure that enables reproducible, cross-database +analyses. With recent updates, `dbparser` has evolved into an **integration engine**, allowing you to merge mechanistic data (DrugBank) with real-world phenotypic data (OnSIDES) and drug-drug interaction risks (TWOSIDES). -That should help in: +## Installation -- working with single data object and not multiple databases in - different formats, -- using R analysis capabilities easily on drugs data, -- 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. +``` r +# From CRAN (stable) +install.packages("dbparser") -## dbparser in Advanced Research +# From GitHub (development) +# install.packages("pak") +pak::pak("ropensci/dbparser") +``` -dbparser serves as data infrastructure for cutting-edge research: +## Supported Databases + +### DrugBank (The Mechanistic Hub) + +[DrugBank](https://go.drugbank.com/) is a comprehensive database +containing detailed drug, pharmacological, and target information. As +both a bioinformatics and a cheminformatics resource, DrugBank combines +detailed drug data (chemical, pharmacological, pharmaceutical) with +comprehensive drug target information (sequence, structure, pathway). +More information can be found [here](https://go.drugbank.com/about). + +- **Parser:** `parseDrugBank()` +- **Input:** Full XML database + ([download](https://go.drugbank.com/releases/latest) — requires free + [account](https://go.drugbank.com/public_users/sign_up), may take a + couple of days) +- **Tested versions:** 5.1.0 through 5.1.12 +- **Alternative:** Use + [dbdataset](https://interstellar-egypt.github.io/dbdataset/) for + pre-parsed data without downloading the XML (GitHub only, exceeds CRAN + size limit) +- **Tutorial:** [DrugBank Parsing + Vignette](https://docs.ropensci.org/dbparser/articles/dbparser.html) + +If you find errors with any DrugBank version, please submit an issue +[here](https://github.com/ropensci/dbparser/issues). -- **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 +### OnSIDES (Adverse Drug Events) -## dvobject Structure +[OnSIDES](https://onsidesdb.org/) provides adverse drug events extracted +from thousands of FDA drug labels using machine learning. -`dvobject` introduces a unified and compressed format of drugs data. It -is an R list object. +- **Parser:** `parseOnSIDES()` +- **Input:** Directory containing OnSIDES CSV files -**For a single database (e.g., DrugBank):** It contains one or more of -the following sub-lists: +### TWOSIDES (Drug-Drug Interactions) -- **drugs**: list of data.frames that contain drugs information - (i.e. synonyms, classifications, …) and it is the only mandatory list -- **salts**: data.frame contains drugs salts information -- **products**: data.frame of commercially available drugs products in - the world -- **references**: data.frame of articles, links and textbooks about - drugs or CETT data -- **cett**: list of data.frames contain targets, enzymes, carriers and - transporters information - -**For a merged database (Integrated Pharmacovigilance):** When databases -are merged using `merge_drugbank_onsides` or `merge_drugbank_twosides`, -the `dvobject` becomes a nested structure containing: - -- **drugbank**: The mechanistic hub. -- **onsides**: The side-effect data (from FDA labels). -- **twosides**: The drug-drug interaction data. -- **integrated_data**: Enriched tables that bridge the databases (e.g., - linking DrugBank IDs to OnSIDES adverse events). -- **metadata**: Detailed provenance for all contained datasets. - -## Drug Databases - -Parsers are available for the following databases (it is in progress -list) - -### 1. DrugBank - -[DrugBank](https://go.drugbank.com/) database is a comprehensive, freely -accessible, online database containing information on drugs and drug -targets. As both a bioinformatics and a cheminformatics resource, -DrugBank combines detailed drug (i.e. chemical, pharmacological and -pharmaceutical) data with comprehensive drug target (i.e. sequence, -structure, and pathway) information. More information about DrugBank can -be found [here](https://go.drugbank.com/about). - -In its raw form, the DrugBank database is a single XML file. Users must -create an [account](https://go.drugbank.com/public_users/sign_up) with -DrugBank and request permission to -[download](https://go.drugbank.com/releases/latest) the database. Note -that this may take a couple of days. - -The `dbparser` package parses the DrugBank XML database into `R` tibbles -that can be explored and analyzed by the user, check [this -tutorial](https://docs.ropensci.org/dbparser/articles/dbparser.html) for -more details. - -If you are waiting for access to the DrugBank database, or do not intend -to do a deep dive with the data, you may wish to use the `dbdataset` -[package](https://interstellar-egypt.github.io/dbdataset/), which -contains the DrugBank database already parsed into `dvobject`. Note that -this is a large package that exceeds the limit set by CRAN. It is only -available on GitHub. - -`dbparser` is tested against DrugBank versions *5.1.0* through *5.1.12* -successfully. If you find errors with these versions or any other -version please submit an issue -[here](https://github.com/ropensci/dbparser/issues). +[TWOSIDES](https://tatonettilab.org/resources/nsides/) provides data on +adverse events arising when two drugs are taken together. -### 2. OnSIDES (The Phenotype) +- **Parser:** `parseTWOSIDES()` +- **Input:** `TWOSIDES.csv.gz` file -[OnSIDES](https://onsidesdb.org/) provides adverse drug events extracted -from thousands of FDA drug labels using machine learning. \* **Parser:** -`parseOnSIDES()` \* **Input:** Directory containing OnSIDES CSV files. +## Quick Start -### 3. TWOSIDES (Polypharmacy) +### Parse a Single Database -[TWOSIDES](https://tatonettilab.org/resources/nsides/) provides data on -drug-drug interactions and the adverse events that arise when two drugs -are taken together. \* **Parser:** `parseTWOSIDES()` \* **Input:** The -`TWOSIDES.csv.gz` file. +``` r +library(dbparser) -## Quick Start: Integration Pipeline +# Parse DrugBank +drugbank_db <- parseDrugBank("data/drugbank.xml") + +# Parse OnSIDES +onsides_db <- parseOnSIDES("data/onsides/") + +# Parse TWOSIDES +twosides_db <- parseTWOSIDES("data/TWOSIDES.csv.gz") +``` + +### Integration Pipeline The power of `dbparser` lies in its ability to chain parsers and mergers together. Here is how you can build a complete pharmacovigilance @@ -158,70 +123,107 @@ 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) %>% +# DrugBank serves as the hub. 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) ``` -For a detailed case study, please refer to the [Integrated -Pharmacovigilance +For a detailed case study, see the [Integrated Pharmacovigilance Vignette](https://docs.ropensci.org/dbparser/articles/drugbank_nside.html). -## Installation +## The dvobject Structure -You can install the released version of dbparser from -[CRAN](https://CRAN.R-project.org) with: +`dvobject` is a unified, compressed format for pharmacological data — an +R list object that preserves complex relational hierarchies while +enabling consistent access patterns. -``` r -install.packages("dbparser") -``` +**For a single database (e.g., DrugBank):** -or you can install the latest updates directly from the repo +- **drugs**: list of data frames containing drug information (synonyms, + classifications, etc.) — the only mandatory component +- **salts**: data frame of drug salt information +- **products**: data frame of commercially available drug products + worldwide +- **references**: data frame of articles, links, and textbooks about + drugs or CETT data +- **cett**: list of data frames containing targets, enzymes, carriers, + and transporters information -``` r -library(devtools) -devtools::install_github("ropensci/dbparser") -``` +**For a merged database (Integrated Pharmacovigilance):** -## Code of Conduct +When databases are merged using `merge_drugbank_onsides` or +`merge_drugbank_twosides`, the `dvobject` becomes a nested structure: -Please note that the ‘dbparser’ project is released with a [Contributor -Code of -Conduct](https://docs.ropensci.org/dbparser/CODE_OF_CONDUCT.html). By -contributing to this project, you agree to abide by its terms. +- **drugbank**: The mechanistic hub +- **onsides**: Side-effect data (from FDA labels) +- **twosides**: Drug-drug interaction data +- **integrated_data**: Enriched tables bridging databases (e.g., linking + DrugBank IDs to OnSIDES adverse events) +- **metadata**: Detailed provenance for all contained datasets -## Contributing Guide +## Research Impact -👍🎉 First off, thanks for taking the time to contribute! 🎉👍 Please -review our [Contributing -Guide](https://docs.ropensci.org/dbparser/CONTRIBUTING.html). +`dbparser` has enabled **10+ peer-reviewed publications** in leading +journals: + +| Domain | Journal | Reference | +|----|----|----| +| Alzheimer’s Drug Repurposing | *Nature Scientific Reports* | Parolo et al. (2023) | +| COVID-19 Therapeutics | *Pharmaceutics* | Pérez-Moraga et al. (2021) | +| Pan-Cancer Biomarkers | *Briefings in Bioinformatics* | Mercatelli et al. (2022) | +| Pathway Modeling | *Computer Methods and Programs in Biomedicine* | Hammoud et al. (2025) | +| Clinical Trial Analysis | *Frontiers in Pharmacology* | Namiot et al. (2023) | + +📊 **50,000+ CRAN downloads** \| Featured in the [CRAN Epidemiology Task +View](https://cran.r-project.org/web/views/Epidemiology.html) -## Share the love ❤️ +For the full list, see our [JOSS +paper](https://joss.theoj.org/papers/3212f2fb07013b8fb1cec499bb9e8381). -Think **dbparser** is useful? Let others discover it, by telling them in -person, via Twitter or a blog post. +## Ecosystem -Using **dbparser** for a paper you are writing? Consider citing it +| Package | Description | Links | +|----|----|----| +| [dbdataset](https://interstellar-egypt.github.io/dbdataset/) | Pre-parsed DrugBank datasets ready for analysis | [GitHub](https://github.com/interstellar-egypt/dbdataset) | +| [covid19dbcand](https://github.com/interstellar-egypt/covid19dbcand) | COVID-19 drug candidate datasets | [GitHub](https://github.com/interstellar-egypt/covid19dbcand) | +| [periscope2](https://periscopeapps.org/) | Shiny framework for interactive dashboards | [CRAN](https://cran.r-project.org/package=periscope2) | + +## Citation + +If you use `dbparser` in published research, please cite our JOSS paper: + + Ali et al., (2026). dbparser: An R Package for Parsing and Integrating + Pharmacological Databases. Journal of Open Source Software, 11(118), + 9950, https://doi.org/10.21105/joss.09950 ``` r 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}, -#> } ``` + +If you find `dbparser` useful, consider ⭐ starring the [GitHub +repository](https://github.com/ropensci/dbparser) and sharing it with +colleagues. + +## Enterprise Support + +For custom database integrations, enterprise support, training, or +deployment assistance — `dbparser` is maintained by [Interstellar +Consultation Services](https://interstellar-egypt.com). + +📧 + +## Contributing + +We welcome contributions! Please review our [Contributing +Guide](https://docs.ropensci.org/dbparser/CONTRIBUTING.html). + +Please note that the `dbparser` project is released with a [Contributor +Code of +Conduct](https://docs.ropensci.org/dbparser/CODE_OF_CONDUCT.html). By +contributing to this project, you agree to abide by its terms. + +[![ropensci_footer](https://ropensci.org/public_images/github_footer.png)](https://ropensci.org) diff --git a/codemeta.json b/codemeta.json index 5219a460..0d2e5ede 100644 --- a/codemeta.json +++ b/codemeta.json @@ -173,7 +173,9 @@ ], "name": "DrugBank Database XML Parser", "url": "https://CRAN.R-project.org/package=dbparser", - "description": "R package version 2.0.1" + "description": "An R Package for Parsing and Integrating +Pharmacological Databases. Journal of Open Source Software, 11(118), +9950, https://doi.org/10.21105/joss.09950" } ], "releaseNotes": "https://github.com/ropensci/dbparser/blob/master/NEWS.md", diff --git a/docs/index.html b/docs/index.html index 83b87f81..8427e617 100644 --- a/docs/index.html +++ b/docs/index.html @@ -65,95 +65,96 @@

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 JOSS Paper DOI

+

CRAN_Status_Badge JOSS Paper rOpenSci Peer-Reviewed DOI codecov metacran downloads CII Best Practices Lifecycle: stable

Overview

-

Drugs databases vary too much in their formats and structures which making related data analysis not a very easy job and requires a lot of efforts to work on only two databases together such as DrugBank, OnSIDES, and TWOSIDES.

-

Hence, dbparser package aims to parse different public drugs databases into a single and unified format R object called dvobject (stands for drugverse object).

+

dbparser is an rOpenSci peer-reviewed R package that parses and integrates major pharmacological databases into standardized, analysis-ready R objects called dvobjects (drugverse objects).

+

Pharmacological databases use incompatible formats and structures, forcing researchers to write custom parsing scripts — a process that consumes 60–80% of analysis time. dbparser eliminates this bottleneck with unified parsing functions, chainable merge operations, and a consistent output structure that enables reproducible, cross-database analyses.

With recent updates, dbparser has evolved into an integration engine, allowing you to merge mechanistic data (DrugBank) with real-world phenotypic data (OnSIDES) and drug-drug interaction risks (TWOSIDES).

-

That should help in:

-
    -
  • working with single data object and not multiple databases in different formats,
    • -
  • using R analysis capabilities easily on drugs data,
    • -
  • 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.
    • -
-

dbparser in Advanced Research +

Installation

-

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
    • -
+
+# From CRAN (stable)
+install.packages("dbparser")
+
+# From GitHub (development)
+# install.packages("pak")
+pak::pak("ropensci/dbparser")
-

dvobject Structure +

Supported Databases

-

dvobject introduces a unified and compressed format of drugs data. It is an R list object.

-

For a single database (e.g., DrugBank): It contains one or more of the following sub-lists:

+
+

DrugBank (The Mechanistic Hub) +

+

DrugBank is a comprehensive database containing detailed drug, pharmacological, and target information. As both a bioinformatics and a cheminformatics resource, DrugBank combines detailed drug data (chemical, pharmacological, pharmaceutical) with comprehensive drug target information (sequence, structure, pathway). More information can be found here.

  • -drugs: list of data.frames that contain drugs information (i.e. synonyms, classifications, …) and it is the only mandatory list
    • +Parser: parseDrugBank() +
  • -salts: data.frame contains drugs salts information
    • +Input: Full XML database (download — requires free account, may take a couple of days)
  • -products: data.frame of commercially available drugs products in the world
    • +Tested versions: 5.1.0 through 5.1.12
  • -references: data.frame of articles, links and textbooks about drugs or CETT data
    • +Alternative: Use dbdataset for pre-parsed data without downloading the XML (GitHub only, exceeds CRAN size limit)
  • -cett: list of data.frames contain targets, enzymes, carriers and transporters information
    • +Tutorial: DrugBank Parsing Vignette +
-

For a merged database (Integrated Pharmacovigilance): When databases are merged using merge_drugbank_onsides or merge_drugbank_twosides, the dvobject becomes a nested structure containing:

+

If you find errors with any DrugBank version, please submit an issue here.

+
+
+

OnSIDES (Adverse Drug Events) +

+

OnSIDES provides adverse drug events extracted from thousands of FDA drug labels using machine learning.

  • -drugbank: The mechanistic hub.
    • -
  • -onsides: The side-effect data (from FDA labels).
    • +Parser: parseOnSIDES() +
  • -twosides: The drug-drug interaction data.
    • +Input: Directory containing OnSIDES CSV files +
+
+
+

TWOSIDES (Drug-Drug Interactions) +

+

TWOSIDES provides data on adverse events arising when two drugs are taken together.

+
  • -integrated_data: Enriched tables that bridge the databases (e.g., linking DrugBank IDs to OnSIDES adverse events).
    • +Parser: parseTWOSIDES() +
  • -metadata: Detailed provenance for all contained datasets.
    • +Input: TWOSIDES.csv.gz file
+
-

Drug Databases +

Quick Start

-

Parsers are available for the following databases (it is in progress list)

-
-

1. DrugBank -

-

DrugBank database is a comprehensive, freely accessible, online database containing information on drugs and drug targets. As both a bioinformatics and a cheminformatics resource, DrugBank combines detailed drug (i.e. chemical, pharmacological and pharmaceutical) data with comprehensive drug target (i.e. sequence, structure, and pathway) information. More information about DrugBank can be found here.

-

In its raw form, the DrugBank database is a single XML file. Users must create an account with DrugBank and request permission to download the database. Note that this may take a couple of days.

-

The dbparser package parses the DrugBank XML database into R tibbles that can be explored and analyzed by the user, check this tutorial for more details.

-

If you are waiting for access to the DrugBank database, or do not intend to do a deep dive with the data, you may wish to use the dbdataset package, which contains the DrugBank database already parsed into dvobject. Note that this is a large package that exceeds the limit set by CRAN. It is only available on GitHub.

-

dbparser is tested against DrugBank versions 5.1.0 through 5.1.12 successfully. If you find errors with these versions or any other version please submit an issue here.

-
-

2. OnSIDES (The Phenotype) +

Parse a Single Database

-

OnSIDES provides adverse drug events extracted from thousands of FDA drug labels using machine learning. * Parser: parseOnSIDES() * Input: Directory containing OnSIDES CSV files.

+
+library(dbparser)
+
+# Parse DrugBank
+drugbank_db <- parseDrugBank("data/drugbank.xml")
+
+# Parse OnSIDES
+onsides_db <- parseOnSIDES("data/onsides/")
+
+# Parse TWOSIDES
+twosides_db <- parseTWOSIDES("data/TWOSIDES.csv.gz")
-

3. TWOSIDES (Polypharmacy) +

Integration Pipeline

-

TWOSIDES provides data on drug-drug interactions and the adverse events that arise when two drugs are taken together. * Parser: parseTWOSIDES() * Input: The TWOSIDES.csv.gz file.

-
-
-
-

Quick Start: Integration Pipeline -

The power of dbparser lies in its ability to chain parsers and mergers together. Here is how you can build a complete pharmacovigilance dataset:

-
+
 library(dbparser)
 library(dplyr)
 
@@ -163,58 +164,150 @@ 
Quick Start: Integration Pipelinetwosides_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) %>% 
+#    DrugBank serves as the hub. 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)

-
For a detailed case study, please refer to the Integrated Pharmacovigilance Vignette.

+
For a detailed case study, see the Integrated Pharmacovigilance Vignette.

+
-

Installation +

The dvobject Structure

-

You can install the released version of dbparser from CRAN with:

-
-install.packages("dbparser")
-

or you can install the latest updates directly from the repo

-
-library(devtools)
-devtools::install_github("ropensci/dbparser")
+

dvobject is a unified, compressed format for pharmacological data — an R list object that preserves complex relational hierarchies while enabling consistent access patterns.

+

For a single database (e.g., DrugBank):

+
    +
  • +drugs: list of data frames containing drug information (synonyms, classifications, etc.) — the only mandatory component
    • +
  • +salts: data frame of drug salt information
    • +
  • +products: data frame of commercially available drug products worldwide
    • +
  • +references: data frame of articles, links, and textbooks about drugs or CETT data
    • +
  • +cett: list of data frames containing targets, enzymes, carriers, and transporters information
    • +
+

For a merged database (Integrated Pharmacovigilance):

+

When databases are merged using merge_drugbank_onsides or merge_drugbank_twosides, the dvobject becomes a nested structure:

+
    +
  • +drugbank: The mechanistic hub
    • +
  • +onsides: Side-effect data (from FDA labels)
    • +
  • +twosides: Drug-drug interaction data
    • +
  • +integrated_data: Enriched tables bridging databases (e.g., linking DrugBank IDs to OnSIDES adverse events)
    • +
  • +metadata: Detailed provenance for all contained datasets
    • +
+
+
+

Research Impact +

+

dbparser has enabled 10+ peer-reviewed publications in leading journals:

+ +++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
DomainJournalReference
Alzheimer’s Drug RepurposingNature Scientific ReportsParolo et al. (2023)
COVID-19 TherapeuticsPharmaceuticsPérez-Moraga et al. (2021)
Pan-Cancer BiomarkersBriefings in BioinformaticsMercatelli et al. (2022)
Pathway ModelingComputer Methods and Programs in BiomedicineHammoud et al. (2025)
Clinical Trial AnalysisFrontiers in PharmacologyNamiot et al. (2023)
+

📊 50,000+ CRAN downloads | Featured in the CRAN Epidemiology Task View

+

For the full list, see our JOSS paper.

+
+
+

Ecosystem +

+ +++++ + + + + + + + + + + + + + + + + + + + + + + +
PackageDescriptionLinks
dbdatasetPre-parsed DrugBank datasets ready for analysisGitHub
covid19dbcandCOVID-19 drug candidate datasetsGitHub
periscope2Shiny framework for interactive dashboardsCRAN
-

Code of Conduct +

Citation

-

Please note that the ‘dbparser’ project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

+

If you use dbparser in published research, please cite our JOSS paper:

+
Ali et al., (2026). dbparser: An R Package for Parsing and Integrating
+Pharmacological Databases. Journal of Open Source Software, 11(118),
+9950, https://doi.org/10.21105/joss.09950
+
+citation("dbparser")
+

If you find dbparser useful, consider ⭐ starring the GitHub repository and sharing it with colleagues.

-

Contributing Guide +

Enterprise Support

-

👍🎉 First off, thanks for taking the time to contribute! 🎉👍 Please review our Contributing Guide.

+

For custom database integrations, enterprise support, training, or deployment assistance — dbparser is maintained by Interstellar Consultation Services.

+

📧

-

Share the love ❤️ +

Contributing

-

Think dbparser is useful? Let others discover it, by telling them in person, via Twitter or a blog post.

-

Using dbparser for a paper you are writing? Consider citing it

-
-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},
-#>   }
+

We welcome contributions! Please review our Contributing Guide.

+

Please note that the dbparser project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

+

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