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🧬 Drug Discovery AI

Multi-agent precision medicine pipeline for novel drug discovery — generating molecules, predicting selectivity, matching clinical trials, and persisting discoveries in under 60 seconds.

Backend CI Frontend CI

🚀 What This Is

Drug Discovery AI is a 18-agent pipeline that takes a gene mutation query (e.g. EGFR T790M) and:

  1. Parses the mutation with LLM + regex fallback
  2. Fetches literature (PubMed), proteins (UniProt), structures (RCSB), compounds (PubChem) in parallel
  3. Downloads PDB structures and detects binding pockets (fpocket / centroid fallback)
  4. Generates novel molecules via RDKit scaffold hopping + bioisostere SMARTS mutations
  5. Docks molecules (Gnina → Vina → AI hash fallback)
  6. Dual-docks vs off-target proteins to compute selectivity ratio — the key differentiator
  7. Screens ADMET (Lipinski + PAINS + toxicophore images)
  8. Optimizes leads with scaffold hopping, bioisostere replacement, and fragment growing — building an evolution tree
  9. Forecasts resistance mutations with LLM
  10. Matches active clinical trials via ClinicalTrials.gov API v2
  11. Builds a knowledge graph and reasoning trace
  12. Saves discoveries to Neon PostgreSQL and exposes a full REST API

📁 Project Structure

drug-discovery-ai/
├── backend/          # Python 3.11 + FastAPI + 18 agents
│   ├── agents/       # 18 pipeline agents
│   ├── pipeline/     # LangGraph state + orchestrator
│   ├── utils/        # LLM router, DB, ADMET, molecule utils
│   ├── routers/      # FastAPI route handlers
│   ├── data/         # JSON data files (curated profiles, resistance, etc.)
│   └── evaluation/   # Benchmark runner
│
├── frontend/         # Next.js 16 + TypeScript + Tailwind v4
│   └── app/
│       ├── components/   # All UI components (analysis, landing, settings)
│       ├── hooks/        # useSSEStream, useAnalysis, useDiscoveries, useTheme
│       ├── lib/          # api.ts, types.ts, utils.ts, theme.ts
│       ├── analysis/     # [sessionId] analysis page (10 tabs)
│       ├── discoveries/  # Discovery library browser
│       └── settings/     # Theme customizer, pipeline config, API key checker
│
├── .github/
│   └── workflows/    # backend-ci.yml + frontend-ci.yml
├── start.sh          # Unix quick-start
├── start.bat         # Windows quick-start
└── README.md

⚡ Quick Start

Prerequisites

  • Python 3.11+
  • Node.js 20+
  • (Optional) AutoDock Vina or Gnina for real docking
  • (Optional) fpocket for pocket detection

1. Backend

cd backend
python -m venv .venv
source .venv/bin/activate   # Windows: .venv\Scripts\activate
pip install -r requirements.txt
cp .env.example .env
# Fill in at least GROQ_API_KEY (free at console.groq.com)
uvicorn main:app --reload --port 8000

2. Frontend

cd frontend
npm install
cp .env.local.example .env.local
npm run dev

Open http://localhost:3000.

🔑 Environment Variables

Backend (backend/.env)

Variable Required Description
OPENAI_API_KEY Optional GPT-4o-mini (primary LLM)
GROQ_API_KEY Recommended Llama 3.3 70B — free at console.groq.com
TOGETHER_API_KEY Optional Mistral 7B fallback
NCBI_API_KEY Optional Higher PubMed rate limits
LANGCHAIN_API_KEY Optional LangSmith observability
DATABASE_URL Optional Neon PostgreSQL connection string
AUTO_SAVE_DISCOVERIES Optional Set true to auto-persist every run

Frontend (frontend/.env.local)

NEXT_PUBLIC_API_URL=http://localhost:8000
NEXT_PUBLIC_APP_NAME="Drug Discovery AI"

🧪 Tech Stack

Backend

  • FastAPI 0.115 + uvicorn + SSE streaming
  • LangGraph 0.2 pipeline orchestration
  • LangSmith observability (enterprise trace dashboard)
  • RDKit — molecule generation, ADMET, depiction
  • Multi-LLM fallback: OpenAI GPT-4o-mini → Groq Llama 3.3 70B → Together Mistral 7B → deterministic template
  • Neon PostgreSQL via SQLAlchemy asyncio + asyncpg
  • External APIs: PubMed, UniProt, RCSB, PubChem, ClinicalTrials.gov

Frontend

  • Next.js 16 App Router + TypeScript strict
  • Tailwind CSS v4 + amber minimal theme system
  • GSAP 3.12 + ScrollTrigger — hero animations, counter reveals
  • Framer Motion 11 — agent status stagger, 2D/3D crossfade
  • D3.js 7 — knowledge graph force-directed, evolution tree
  • Recharts — ADMET radar chart, docking score chart
  • shadcn/ui (Radix primitives) — accessible components
  • NGL — 3D molecular viewer
  • Biome — linting + formatting (no ESLint, no Prettier)

🏆 Win Factor Features

Feature Description
Selectivity Ratio Dual-docks top leads vs. off-target proteins. ratio = target / off-target. ≥3.0 = High selectivity
Evolution Tree D3 visualization of how seed molecules transform through scaffold hopping + bioisostere operations
LangSmith Tracing Enterprise observability — every agent call, token count, latency, auditable in real time
Clinical Trial Matching Live ClinicalTrials.gov API — links discovery to active patient trials
Resistance Forecasting LLM predicts which secondary mutations will emerge under treatment pressure
Discovery Database Neon PostgreSQL — persists all discoveries, browseable in the Discoveries Library

🗣️ Demo Script (Hackathon)

  1. "Meet Sarah. 52 years old. Lung cancer. EGFR T790M. Erlotinib stopped working."
  2. Type EGFR T790M → Launch Analysis
  3. Watch 18 agents stream live in the PipelineStatus panel
  4. Top Leads tab: "Our AI generated molecules that don't exist in any database. This one binds 3.2× harder to the cancer target than to healthy ABL1 kinase."
  5. Evolution Tree: "Here's exactly how the seed molecule was transformed — scaffold hop +1.4 kcal/mol, bioisostere +0.8 kcal/mol."
  6. Clinical Trials: "3 active Phase II trials targeting EGFR T790M. Our molecule targets the same pocket."
  7. Open LangSmith tab: "Every decision, auditable. Enterprise ready."
  8. Click Save Discovery → "Permanently saved to our Neon database."

🛠️ Development

Lint & Format

# Backend
cd backend
ruff check .
mypy .

# Frontend
cd frontend
npm run check        # Biome check
npm run check:fix    # Auto-fix
npm run typecheck    # TypeScript

Run Benchmark

cd backend
python -c "
from evaluation.benchmark_runner import run_benchmark_cases
import asyncio
r = asyncio.run(run_benchmark_cases())
print(f'Accuracy: {r[\"accuracy\"]*100:.0f}%')
"

Verify SelectivityAgent

cd backend
python -c "
from agents.SelectivityAgent import SelectivityAgent
import asyncio
r = asyncio.run(SelectivityAgent().run({
    'docking_results': [{'smiles': 'CC(=O)Nc1ccc(O)cc1', 'binding_energy': -8.5, 'compound_name': 'Test'}],
    'mutation_context': {'gene': 'EGFR'},
    'analysis_plan': type('P', (), {'run_selectivity': True})(),
}))
print('SelectivityAgent OK:', r['selectivity_results'][0]['selectivity_label'])
"

🌿 Git Branches

Branch Owner Scope
main — Protected. Merge via PR only
feature/backend-agents Backend dev Python agents, pipeline, bioinformatics
feature/frontend-ui Frontend dev Next.js pages, components, GSAP
feature/testing-validation QA dev Pytest, benchmark, E2E validation

📄 License

MIT — built for a hackathon. Not for clinical use. All results are computational predictions only.