🧬 OrphaFold

AI-powered research platform for rare diseases

Multi-agent architecture • Biomedical APIs • Comprehensive reports

Quick Start • Architecture • Devpost Submission

OrphaFold: Deep Structural Search for Orphan Diseases

Note

OrphaFold is a Research Prototype and Proof of Concept (PoC). It is intended for professional researchers and geneticists as a decision-support tool, not as a clinical system.

OrphaFold is an AI-powered platform designed to accelerate research into orphan diseases by combining real-time API enrichment with advanced Multi-Agent orchestration and structural biology.

👁️ The "Why" (Vision)

Today, 300 million people live with a rare disease. Yet, 95% of these 7,000+ conditions have no approved treatment. This is largely driven by a chronic lack of funding, economic barriers, and fragmented knowledge trapped in silos.

• Structural Intelligence: Using protein structures (AlphaFold) as a key baseline for discovery.
• Binding Pocket Analysis: Performing comparative analysis of protein binding pockets and 3D folds.
• Democratizing Repurposing: Identifying hidden connections between existing drugs and rare proteins through structural homology.

📸 Interface Preview

🏠 Search & Home	🧪 Biological Insights
💊 Hypothesis Lab	🔬 Research Tracking
👁️ Project Vision	🧬 Structural Analysis
🧪 Cross-Disease Insights	� Structural Proteomics (AlphaFold)
📚 Bibliography Management

🧬 The Agent Architecture

OrphaFold orchestrates a 4-agent pipeline to analyze orphan pathologies from multiple biological perspectives:

1. 🏥 Clinical Grounding Agent

• Purpose: Establishes the clinical baseline using direct REST APIs.
• APIs & Tools: Orphanet (orphadata.com), OMIM (NCBI E-utilities), Google Search Grounding.
• Output: Prevalence, inheritance patterns, and disease classifications.

2. 🧪 Bio-Mechanism Agent

• Purpose: Uncovers the molecular pathophysiology and structural machinery.
• APIs & Tools: UniProt, NCBI Gene, ClinVar, AlphaFold DB.
• Output: Target proteins, functional domains, pLDDT confidence, and druggability assessments.

3. 🔬 Discovery Agent

• Purpose: Connects the disease to the broader research and clinical landscape.
• APIs & Tools: PubMed (NCBI), ClinicalTrials.gov, structural homology search.
• Output: Active trials, synthesized bibliography, and cross-disease insights.

4. 💊 Drug Repurposing Agent (The Synthesis Catalyst)

• Purpose: Proposes therapeutic candidates by bridging mechanism overlap via Structural Homology (On-Demand).
• APIs & Tools: DrugBank, ChEMBL, Reasoning Engine (thinking budget), 3D Binding Pocket analysis.
• Output: In-silico repurposing hypotheses with feasibility scores based on 3D fold similarity and catalytic site mapping.

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🚀 How it Works

1. Input: The user enters a rare disease name or description (e.g., "Cystic Fibrosis").
2. API Enrichment: The system acts as a "Pre-Flight" layer, simultaneously querying:
   • Orphanet, OMIM, UniProt, NCBI Gene, ClinVar, PubMed.
3. Agent Orchestration: The gathered context is fed into the Multi-Agent System powered by Gemini 1.5 Pro.
4. Synthesis & Homology: The agents reason over the data, perform structural homology analysis to identify shared binding motifs, and generate a structured report.
5. Visualization: The frontend renders interactive molecular structures (pdb), clinical data cards, and research timelines directly from the AlphaFold DB.

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💻 Installation

Prerequisites: Node.js (v18+)

1. Clone the repository:

   git clone https://github.com/Paulhb7/orphafold.git
   cd orphafold

2. Install dependencies:

   make install
   # OR
   npm install

3. Configure Environment:

   • Create a .env.local file in the root directory.
   • Add your Gemini API Key:

     GEMINI_API_KEY=your_api_key_here

4. Run Locally:

   make dev
   # OR
   npm run dev

🌍 Deployment

This project is optimized for deployment on Google AI Studio.

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🚀 Next Steps

Our journey from discovery to impact continues with the following roadmap:

• Direct Docking Simulations: Integrate in-silico simulations directly within the agentic loop to transform hypotheses into predictive scores.
• Agent Development Kit (ADK): Transition to the ADK framework to leverage more comprehensive orchestration and modularity.
• Pilot Beta Tests: Collaborate with geneticists and rare disease researchers to validate the platform's utility in real-world research scenarios.
• Scale Data Sources: Expand the agent pipeline to include more specialized biomedical repositories and real-world evidence (RWE) data.