Deep Genomic Discovery for Rare Diseases

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.

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.

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

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

graph TD
    UI[User Interface / React]
    
    Orchestrator[Gemini 1.5 Pro Orchestrator]
    
    UI -->|Disease Name/Search| Orchestrator
    
    Orchestrator --> clinicalAgent[🏥 Clinical Grounding Agent]
    Orchestrator --> bioAgent[🧪 Bio-Mechanism Agent]
    Orchestrator --> discoveryAgent[🔬 Discovery Agent]
    Orchestrator --> repurposingAgent[💊 Drug Repurposing Agent]
    
    clinicalAgent -.->|Query| API1[(Orphanet / OMIM)]
    bioAgent -.->|Query| API2[(UniProt / AlphaFold DB)]
    discoveryAgent -.->|Query| API3[(PubMed / ClinicalTrials)]
    repurposingAgent -.->|Query| API4[(DrugBank / ChEMBL)]
    
    clinicalAgent --> Synthesis[Synthesis & Report Generation]
    bioAgent --> Synthesis
    discoveryAgent --> Synthesis
    repurposingAgent --> Synthesis
    
    Synthesis -->|Structured Data| UI

• 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.

• 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.

• 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.

• 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.

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.

sequenceDiagram
    participant User
    participant App as React App
    participant APIs as Pre-Flight External APIs
    participant Orchestrator as Gemini Orchestrator
    participant Agents as Multi-Agent System
    
    User->>App: Input Rare Disease Name
    App->>APIs: Query (Orphanet, OMIM, UniProt, etc.)
    APIs-->>App: Raw Biological/Clinical Data
    App->>Orchestrator: Send Context & User Goal
    Orchestrator->>Agents: Delegate Specific Tasks
    
    par Clinical Analysis
        Agents->>Agents: Clinical Grounding
    and Bio Analysis
        Agents->>Agents: Bio-Mechanism Mapping
    and Discovery
        Agents->>Agents: Literature & Trial Search
    and Repurposing
        Agents->>Agents: Structural Homology
    end
    
    Agents-->>Orchestrator: Consolidated Findings
    Orchestrator-->>App: Structured Output
    App->>App: Render Molecular Structures (PDB)
    App-->>User: Display Interactive Dashboard

Prerequisites: Node.js (v18+)

1. Clone the repository:

   git clone https://github.com/ankush850/Orphafold_AI.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

This project is optimized for deployment on Google AI Studio.

Today, over 300 million people live with a rare disease. Yet, 95% of these 7,000+ conditions have no approved treatment. This crisis is largely driven by a chronic lack of funding, economic barriers, and fragmented knowledge trapped in data silos. We were inspired to bridge this gap by leveraging modern AI to democratize drug repurposing and provide actionable, structured insights for researchers and geneticists working tirelessly to find cures.

OrphaFold AI is a research platform that accelerates discovery in orphan diseases by combining real-time API enrichment with advanced Multi-Agent orchestration. When a researcher inputs a rare disease, the platform acts as a "Pre-Flight" layer to query multiple biological APIs. It then delegates specialized analysis to four intelligent agents:

1. Clinical Grounding Agent: Establishes the clinical baseline (prevalence, inheritance).
2. Bio-Mechanism Agent: Uncovers molecular pathophysiology and structural machinery.
3. Discovery Agent: Connects the disease to active trials and clinical landscape.
4. Drug Repurposing Agent: Proposes therapeutic candidates via 3D structural homology and binding pocket analysis.

The platform is powered by a robust stack:

• Orchestration Engine: We utilized Gemini 1.5 Pro to manage and orchestrate the complex interactions of our 4-agent system.
• Frontend: Built a responsive, interactive UI using React and TypeScript to render molecular structures and clinical data cards.
• Data Integration: Integrated a wide array of REST APIs including Orphanet, OMIM, UniProt, AlphaFold DB, PubMed, ClinicalTrials.gov, DrugBank, and ChEMBL.

• Data Silos & API Integration: Normalizing data across so many different biological databases (from genetic data to protein structures) proved challenging.
• Agent Orchestration: Ensuring that the Gemini agents collaborated effectively without hallucinations, maintaining strict adherence to the biological context.
• Structural Analysis in Real-Time: Incorporating 3D binding pocket analysis and structural homology scoring in a way that feels fluid and responsive on the UI.

• Successfully orchestrating a 4-agent AI pipeline that handles specialized biomedical reasoning flawlessly.
• Integrating real-time 3D protein visualization (via AlphaFold DB) directly into the decision-support dashboard.
• Creating a synthesis loop that automatically generates highly plausible drug repurposing hypotheses based on structural overlap, rather than just literature scraping.

• We gained deep insights into the complexity of orphan diseases and the structural biology behind them.
• We learned advanced prompt engineering and agent delegation techniques to keep LLMs strictly grounded in external API data.
• We realized the immense potential for AI to break down barriers in neglected fields of medicine.

• Direct Docking Simulations: Integrating in-silico simulations directly within the agentic loop to transform our structural hypotheses into predictive, quantitative scores.
• Agent Development Kit (ADK): Transitioning to a more comprehensive orchestration and modularity framework.
• Pilot Beta Tests: Collaborating directly with geneticists and rare disease researchers to validate our platform's utility in real-world scenarios.
• Scale Data Sources: Expanding our data ingestion to include real-world evidence (RWE) and even more specialized genomic repositories.

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.