Agentic AI Platform for Drug Repurposing & Pharma Intelligence
PharmaIntel is a full-stack, agentic AI–powered research platform designed to accelerate drug repurposing, innovation research, and strategic decision-making in the pharmaceutical domain.
It orchestrates multiple AI agents to analyze clinical trials, patents, market data, trade intelligence, and internal documents, delivering decision-ready insights through a modern web dashboard.
- 🔍 Natural Language Research Queries
- 🤖 Agentic AI Orchestration (LangGraph-based)
- 🧪 Clinical Trials Intelligence (Mocked ClinicalTrials.gov)
- 📜 Patent Landscape & Freedom-to-Operate Analysis
- 📊 Market & EXIM Trade Insights
- 📁 Internal Document Summarization
- 📈 Visual Dashboards & PDF Report Generation
- 🧠 Explainable, Auditable AI Outputs
High-level Flow:
PharmaIntel follows a layered, agent-driven architecture designed for scalability, explainability, and modular expansion.
The system orchestrates multiple domain-specific AI agents through a centralized master agent to deliver decision-ready pharmaceutical intelligence.
- Web-based React dashboard for natural language research queries
- Real-time visualization of insights, charts, and reports
- Agent execution status, logs, and traceability
- Modular UI for future feature expansion
- Interprets and parses user intent
- Decomposes complex queries into domain-specific tasks
- Manages workflow state using LangGraph
- Coordinates parallel execution of worker agents
- Aggregates intermediate results for synthesis
Each agent operates independently and in parallel to maximize efficiency:
- Clinical Trials Agent – Analyzes trial phases, outcomes, and trends
- Patent Landscape Agent – Evaluates patent status, lifecycle, and FTO risks
- Market Intelligence Agent – Assesses market size, growth, and competition
- EXIM Trade Agent – Extracts import–export and trade flow insights
- Web Intelligence Agent – Mines scientific publications and guidelines
- Internal Knowledge Agent – Summarizes internal documents and reports
- Cross-domain insight correlation and validation
- Confidence-weighted result aggregation
- Automated generation of:
- Interactive dashboards
- Visual analytics (charts, heatmaps)
- Decision-ready PDF research reports
- Containerized services using Docker
- Modular, cloud-ready deployment
- Designed for scalability across AWS, Azure, or GCP
- Secure and auditable execution flow
Design Philosophy:
Parallel intelligence, deterministic orchestration, and explainable outputs — built for real-world pharma decision-making.
- React.js
- TypeScript
- Modern UI components & data visualizations
- Agent execution status & logs
- Python
- FastAPI
- LangGraph for stateful agent orchestration
- Modular AI agent architecture
- LLM-based reasoning
- Mocked APIs simulating:
- ClinicalTrials.gov
- Patent databases (USPTO-like)
- Market intelligence platforms
- EXIM trade data
- Internal document processing
- Docker-based modular setup
- Cloud-ready architecture
pharmintel/
│
├── frontend/ # React + TypeScript frontend
│ ├── components/
│ ├── pages/
│ ├── services/
│ └── visualizations/
│
├── backend/ # FastAPI backend
│ ├── agents/ # Individual AI agents
│ ├── orchestration/ # LangGraph workflows
│ ├── api/ # REST endpoints
│ ├── services/
│ └── utils/
│
├── docker/ # Docker configuration
├── README.md
└── requirements.txt
The platform delivers insight-rich, decision-ready outputs through interactive and automated visualizations, including:
- Bar Charts, Donut Charts, and Heatmaps for:
- Patent lifecycle distribution
- Market opportunity analysis
- Clinical trial phase breakdown
- Agent-wise execution timelines for transparency and traceability
- Auto-generated PDF research reports summarizing cross-domain insights
- Mock data sources are acceptable for MVP and hackathon implementations
- User queries are molecule-centric or indication-centric
- Human validation is available for high-impact or critical decisions
- Paid real-world data sources are not directly integrated
- LLM context length and reasoning limitations
- Outputs must remain explainable, auditable, and transparent
- Drug repurposing opportunity analysis
- Patent landscape and Freedom-to-Operate (FTO) assessment
- Market entry strategy and trade intelligence
- End-to-end pharmaceutical research acceleration
- Strategic decision support for R&D and innovation teams
- ✅ MVP ready
- 🔄 Mocked data integrations in place
- 🚧 Real-world data connectors planned
- Live API integrations (ClinicalTrials.gov, patent databases)
- Role-based access control (RBAC)
- Advanced financial and commercial modeling
- Multi-language query and report support
- Cloud-native deployment (AWS / Azure / GCP)
This is a Next.js project bootstrapped with create-next-app.
A Multi-Agent System (MAS) designed to automate the Hypothesize-Test-Pivot cycle in pharmaceutical R&D.
This solution implements a cyclic LangGraph workflow:
- Planner: Formulates a scientific hypothesis (e.g. "Repurpose Drug X for Y").
- Test (Agents): Parallel execution of specialized agents:
ClinicalAgent: Analyzes trial data and safety signals (ClinicalTrials.gov).PatentAgent: Checks for freedom-to-operate and white space (USPTO).IQVIAAgent: Analyzes market size and CAGR trends.
- Verifier: Evaluating if the findings support the hypothesis or if there are blockers (e.g., toxicity).
- Failure Analysis & Pivot: If verified as "Failure", the AI analyzes why and suggests a Pivot (e.g., "Switch to Inhaled formulation"), restarting the loop.
backend/: FastAPI + LangGraph + Google Geminifrontend/: Next.js 14 + TailwindCSS (Dark Mode Console)
- Google API Key: Put in
backend/.env.
./start.shBackend:
cd backend
uvicorn app.main:app --reloadFrontend:
cd frontend
npm run devOpen http://localhost:3000.