docs: Updated README

README.md

@@ -1,27 +1,145 @@
-DeepPurpose002 — Training & Prediction (DTI)
+# 🧬 Multimodal Biological Design & Discovery Intelligence
 
-Ce repo contient un pipeline DeepPurpose pour :
+> Drug-Target Interaction prediction platform powered by DeepPurpose ML + Qdrant vector search + Next.js visualization
 
-entraîner un modèle Drug–Target Interaction (DTI) à partir de paires (SMILES, séquence protéique, label),
+![Python](https://img.shields.io/badge/Python-3.10-blue)
+![Next.js](https://img.shields.io/badge/Next.js-16-black)
+![CUDA](https://img.shields.io/badge/CUDA-11.8-green)
+![License](https://img.shields.io/badge/License-MIT-yellow)
 
-évaluer le modèle (métriques + logs),
+## 🎯 Overview
 
-prédire des interactions/affinités sur de nouvelles paires et exporter les résultats.
+This platform enables **drug discovery** through:
+- **Deep Learning** — Morgan fingerprints + CNN protein encoding (DeepPurpose)
+- **Vector Search** — Similarity search via Qdrant embeddings
+- **3D Visualization** — Real PCA projections of drug-target space
+- **Interactive UI** — Next.js dashboard with Recharts
 
-Contenu
+## 📊 Model Performance
 
-deeppurpose002.py : chargement données → preprocessing/encodage → entraînement → évaluation → sauvegarde modèle + outputs
+| Dataset | Concordance Index | Pearson | MSE |
+|---------|-------------------|---------|-----|
+| **KIBA** | 0.7003 | 0.5219 | 0.0008 |
+| **BindingDB_Kd** | 0.8083 | 0.7679 | 0.6668 |
+| **DAVIS** | 0.7914 | 0.5446 | 0.4684 |
 
-prediction_test.py (ou équivalent) : chargement du modèle sauvegardé → prédictions → export CSV
+## 🏗️ Architecture
 
-Utilisation
+```
+┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
+│   TDC Dataset   │────▶│   DeepPurpose   │────▶│     Qdrant      │
+│   (KIBA/DAVIS)  │     │  Morgan + CNN   │     │  256D Vectors   │
+└─────────────────┘     └─────────────────┘     └────────┬────────┘
+                                                         │
+┌─────────────────┐     ┌─────────────────┐              │
+│   Next.js UI    │◀────│   FastAPI       │◀─────────────┘
+│   localhost:3000│     │   localhost:8001│   Similarity Search
+└─────────────────┘     └─────────────────┘
+```
+
+## 🚀 Quick Start
+
+### Prerequisites
+- Python 3.10
+- Node.js 18+
+- Docker Desktop (for Qdrant)
+- CUDA 11.8 (optional, for GPU)
+
+### 1. Setup Python Environment
+```bash
+python -m venv .venv
+.venv\Scripts\activate  # Windows
+pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
+pip install DeepPurpose qdrant-client fastapi uvicorn scikit-learn
+```
+
+### 2. Start Qdrant (Vector Database)
+```bash
+docker run -d --name qdrant -p 6333:6333 -p 6334:6334 qdrant/qdrant:latest
+```
+
+### 3. Ingest Data (One-time)
+```bash
+python ingest_qdrant.py
+# Loads KIBA dataset, generates embeddings, uploads to Qdrant
+# ~23,531 drug-target pairs indexed
+```
+
+### 4. Start API Server
+```bash
+python -m uvicorn server.api:app --host 0.0.0.0 --port 8001
+```
+
+### 5. Start Frontend
+```bash
+cd ui
+pnpm install
+pnpm dev
+# Open http://localhost:3000
+```
+
+## 📁 Project Structure
+
+```
+├── config.py              # Shared configuration (model paths, Qdrant settings)
+├── ingest_qdrant.py       # ETL: TDC → DeepPurpose → Qdrant
+├── deeppurpose002.py      # Model training script
+├── server/
+│   └── api.py             # FastAPI backend (/health, /api/points, /api/search)
+├── runs/
+│   └── 20260125_104915_KIBA/  # Best model checkpoint
+│       ├── model.pt
+│       └── config.pkl
+├── ui/                    # Next.js 16 + Shadcn UI
+│   ├── app/
+│   │   ├── explorer/      # 3D scatter plot visualization
+│   │   ├── discovery/     # Drug discovery interface
+│   │   └── data/          # Data browser
+│   └── lib/
+│       └── explorer-service.ts  # API client
+└── data/
+    └── kiba.tab           # Cached TDC dataset
+```
+
+## 🔌 API Endpoints
+
+| Endpoint | Method | Description |
+|----------|--------|-------------|
+| `/health` | GET | Service health + metrics |
+| `/api/points` | GET | Get 3D PCA points for visualization |
+| `/api/search` | POST | Similarity search by SMILES/sequence |
+
+### Example: Get Points
+```bash
+curl "http://localhost:8001/api/points?limit=100&view=combined"
+```
+
+### Example: Search Similar
+```bash
+curl -X POST "http://localhost:8001/api/search" \
+  -H "Content-Type: application/json" \
+  -d '{"smiles": "CC(=O)OC1=CC=CC=C1C(=O)O", "top_k": 10}'
+```
+
+## 🧪 Training New Models
+
+```bash
+# Edit deeppurpose002.py to change dataset/encoding
 python deeppurpose002.py
-python prediction_test.py
 
-Format attendu
+# Re-ingest with new model
+python ingest_qdrant.py
+```
+
+## 👥 Contributors
+
+- **Hamza Sammoud** — ML Pipeline & Backend
+- **Rami Troudi** — Frontend UI
+
+## 📄 License
 
-Train (supervisé) : drug_smiles, target_sequence, label
+MIT License - See [LICENSE](LICENSE) for details.
 
-Predict : drug_smiles, target_sequence
+---
 
-Outputs : modèles dans models/, résultats/logs dans outputs/.
+*Built for Hackathon 2026* 🏆

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