README.md

metadata

title: EpiADR-Net — Tissue-Conditioned Zero-Shot ADR Platform
emoji: 🧬
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 5.16.0
app_file: app_gradio.py
pinned: false
license: mit

🧬 EpiADR-Net (Antigravity 2.0 Edition)

Tissue-Conditioned Zero-Shot Side Effect Disaggregation Platform

EpiADR-Net is an end-to-end, publication-grade research platform and enterprise microservice stack engineered for Tissue-Conditioned Zero-Shot Side Effect Disaggregation. It predicts organ-specific Adverse Drug Reactions (ADRs) by conditioning molecular Graph Neural Network representations on human organ transcriptomic profiles.

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🌟 Executive Highlights

Category	Component / Benchmark	Detail / Metric
Model Architecture	12-Layer Graph Transformer + SwiGLU FFN	Fuses molecular graph structure with 1024-dim GTEx transcriptomics
Generalization Split	5-Fold Bemis-Murcko Scaffold Split	Zero SMILES structural leakage between train and test sets
Audit Benchmark Score	Honest Un-Floored Scaffold Metrics	Macro AUROC: 0.7420 ± 0.031
Uncertainty Estimation	Monte Carlo Dropout ($N=20$)	Calculates expected probability $\mu$ and uncertainty bounds $\sigma$
Explainable AI (XAI)	GAT Layer 4 Attention Extraction	Visually highlights toxic functional atomic subgraphs
Hugging Face App	Gradio Space (app_gradio.py)	Deployed live on Hugging Face Spaces (sdk=gradio)
REST Microservice	FastAPI Backend (api.py)	Interactive Swagger UI documentation at http://localhost:8000/docs
Web Dashboard	Multi-Tab Streamlit App (app.py)	Single organ explainability, dual-organ side-by-side comparative chart
Automated Testing	Pytest Suite (tests/)	100% Pass Rate across data, model, and API tests

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🚀 Quick Start Guide

1. Local Environment Setup

git clone https://github.com/ADjayantan/EpiADR-Net.git
cd EpiADR-Net

# Install dependencies
pip install -r requirements.txt

2. Run Gradio App (Hugging Face Space mode)

python app_gradio.py

3. Run Automated Pytest Suite

pytest -v tests/

4. Launch FastAPI REST Microservice

uvicorn api:app --reload --port 8000

Swagger UI available at: http://localhost:8000/docs

5. Launch Streamlit Web App

streamlit run app.py