💊 Agentic AI Assistant for Pharmaceutical Research

Overview

This project is an Agentic AI Assistant designed to support pharmaceutical research.
Researchers can query a multi-agent AI backend to get insights on drug discovery, clinical trial analysis, and drug-drug interactions.

The system routes queries to specialized agents, returns step-by-step analysis, visuals (dummy graphs/charts), and research recommendations.

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Features

• Multi-Agent Architecture

  • Coordinator Agent: Parses queries and delegates to specialized agents.
  • Drug Discovery Agent: Analyzes molecular structures and predicts drug-target interactions.
  • Clinical Trial Analysis Agent: Validates trial designs, analyzes sample data, and recommends patient cohorts.
  • Drug Interaction Agent: Detects drug-drug interactions and suggests dosage adjustments.

• Interactive Query Handling

  • Agents can ask follow-up questions to clarify queries.
  • Step-by-step reasoning is included in responses.

• Visual Outputs

  • Graphs, molecular charts, and interaction diagrams (dummy visuals for demonstration).

• Persistent Chat History

  • Stores all conversations in a database for review and auditing.

• Streaming Responses

  • Responses are streamed progressively to simulate a real-time AI assistant.

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Tech Stack

• Backend: FastAPI
• AI Agent Framework: Agno / PydanticAI (dummy logic in this prototype)
• Database: SQLite / Any preferred relational DB
• Containerization: Docker (optional)
• Frontend (Optional): Streamlit for chat interface

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Setup Instructions

1. Clone the repository

   git clone https://github.com/<your-username>/pharma-agentic-ai.git
   cd pharma-agentic-ai

2)Create and activate virtual environment

python -m venv venv source venv/bin/activate # Linux/Mac venv\Scripts\activate # Windows

3)Install dependencies

pip install -r requirements.txt

4)Run FastAPI backend

uvicorn main:app --reload

5. Run Streamlit frontend

streamlit run frontend.py

^)Access the application

FastAPI Swagger UI: http://127.0.0.1:8000/docs

Streamlit UI: http://localhost:8501

6. API Endpoints

POST /query-stream Send a query to the AI assistant and receive a streamed response.

Request Body:

{ "user_query": "Can you analyze compound X?" }

Response: Streamed text containing AI response, steps, visuals, and follow-up.

GET /history Retrieve full conversation history.