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README.md

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 ---
+language:
+- en
 license: mit
+library_name: pytorch
+tags:
+- chemistry
+- graph-neural-networks
+- drug-discovery
+- rdkit
+- pytorch-geometric
+- moleculenet
+- cheminformatics
+- toxicology
+- admet
+- mpnn
+- gin
+- gcn
+- gat
+datasets:
+- esol
+- bbbp
+- tox21
+metrics:
+- rmse
+- roc_auc
+model-index:
+- name: BioGraph-AI-MPNN-ESOL
+  results:
+  - task:
+      type: tabular-regression
+      name: Aqueous Solubility Prediction
+    dataset:
+      name: ESOL Delaney
+      type: esol
+    metrics:
+    - type: rmse
+      value: 1.010
+      name: Test RMSE
+- name: BioGraph-AI-MPNN-Tox21
+  results:
+  - task:
+      type: tabular-classification
+      name: Multi-Task Nuclear Receptor & Stress Pathway Toxicity
+    dataset:
+      name: Tox21
+      type: tox21
+    metrics:
+    - type: roc_auc
+      value: 0.757
+      name: Test ROC-AUC
+- name: BioGraph-AI-RF-BBBP
+  results:
+  - task:
+      type: tabular-classification
+      name: Blood-Brain Barrier Penetration
+    dataset:
+      name: BBBP
+      type: bbbp
+    metrics:
+    - type: roc_auc
+      value: 0.853
+      name: Test ROC-AUC
 ---
+
+# BioGraph AI: Deep Graph Neural Networks for Molecular Property Prediction & ADMET Profiling
+
+[![GitHub Stars](https://img.shields.io/badge/GitHub-BioGraph--AI-10b981?logo=github)](https://github.com/VSRAJHAVEL/BioGraph-AI)
+[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT)
+[![PyTorch](https://img.shields.io/badge/PyTorch-2.0+-ee4c2c?logo=pytorch)](https://pytorch.org/)
+[![PyG](https://img.shields.io/badge/PyG-PyTorch_Geometric-3b82f6)](https://pyg.org/)
+[![RDKit](https://img.shields.io/badge/Cheminformatics-RDKit-green)](https://www.rdkit.org/)
+
+**BioGraph AI** is an enterprise-grade Computer-Aided Drug Design (CADD) and Molecular Machine Learning platform. This repository hosts pre-trained model weights for **15 state-of-the-art graph architectures and classical baselines** evaluated under strict Bemis-Murcko scaffold splitting across three canonical MoleculeNet benchmarks (**ESOL, BBBP, Tox21**).
+
+> **Handcrafted by [RAJHAVEL V S](https://vsrajhavel.github.io)**  
+> **Portfolio:** [vsrajhavel.github.io](https://vsrajhavel.github.io) | **LinkedIn:** [in/rajhavelvs](https://in.linkedin.com/in/rajhavelvs) | **GitHub:** [@VSRAJHAVEL](https://github.com/VSRAJHAVEL)
+
+---
+
+## 🔬 Benchmark Performance (Bemis-Murcko Scaffold Split 80/10/10)
+
+All models were trained, validated, and evaluated on local Intel CPU physical threads. Zero scaffold leakage was mathematically confirmed across all training and test partitions.
+
+### 1. Aqueous Solubility (ESOL — Delaney Regression, $\log S$ in $\text{mol/L}$)
+| Model Architecture | Test RMSE ↓ | Test MAE ↓ | Test $R^2$ ↑ | Checkpoint File |
+| :--- | :---: | :---: | :---: | :--- |
+| **MPNN (Edge-Aware)** | **1.010** | **0.777** | **0.784** | `checkpoints/esol_mpnn_best.pt` |
+| **GIN (Weisfeiler-Lehman)** | 1.054 | 0.812 | 0.764 | `checkpoints/esol_gin_best.pt` |
+| **GCN (Kipf-Welling)** | 1.080 | 0.811 | 0.753 | `checkpoints/esol_gcn_best.pt` |
+| **Baseline (RF + Morgan)** | 1.109 | 0.859 | 0.739 | `checkpoints/esol_baseline_best.pkl` |
+| **GAT (Multi-Head)** | 1.208 | 0.938 | 0.691 | `checkpoints/esol_gat_best.pt` |
+
+### 2. Blood-Brain Barrier Penetration (BBBP — Binary Classification)
+| Model Architecture | Test ROC-AUC ↑ | Test PR-AUC ↑ | Checkpoint File |
+| :--- | :---: | :---: | :--- |
+| **Baseline (RF + Morgan)** | **0.853** | **0.978** | `checkpoints/bbbp_baseline_best.pkl` |
+| **GIN** | 0.723 | 0.936 | `checkpoints/bbbp_gin_best.pt` |
+| **GCN** | 0.719 | 0.951 | `checkpoints/bbbp_gcn_best.pt` |
+| **MPNN** | 0.665 | 0.944 | `checkpoints/bbbp_mpnn_best.pt` |
+| **GAT** | 0.624 | 0.928 | `checkpoints/bbbp_gat_best.pt` |
+
+### 3. Toxicity Profiling (Tox21 — 12 Multi-Task Toxicity Pathways)
+*Evaluated across 12 nuclear receptor (NR) and stress response (SR) pathways with missing label masking.*
+| Model Architecture | Test ROC-AUC ↑ | Test PR-AUC ↑ | Checkpoint File |
+| :--- | :---: | :---: | :--- |
+| **MPNN (Edge-Aware)** | **0.757** | 0.304 | `checkpoints/tox21_mpnn_best.pt` |
+| **GCN** | 0.754 | 0.310 | `checkpoints/tox21_gcn_best.pt` |
+| **GIN** | 0.747 | 0.306 | `checkpoints/tox21_gin_best.pt` |
+| **Baseline (RF)** | 0.730 | **0.349** | `checkpoints/tox21_baseline_best.pkl` |
+| **GAT** | 0.711 | 0.264 | `checkpoints/tox21_gat_best.pt` |
+
+---
+
+## 🚀 Quickstart: Loading Checkpoints in Python
+
+### Installation
+```bash
+pip install torch torch-geometric rdkit huggingface_hub
+```
+
+### Download Pretrained Weights
+```python
+from huggingface_hub import hf_hub_download
+import torch
+
+# Download best ESOL MPNN checkpoint
+checkpoint_path = hf_hub_download(
+    repo_id="VSRAJHAVEL/BioGraph-AI-Models",
+    filename="checkpoints/esol_mpnn_best.pt"
+)
+
+state_dict = torch.load(checkpoint_path, map_location="cpu")
+print("Successfully loaded BioGraph AI MPNN checkpoint:", len(state_dict), "weight tensors")
+```
+
+---
+
+## ���� Architectural Overview
+
+1. **Message Passing Neural Networks (MPNN)**: Formulated with edge-network conditioned message functions $M(h_v, h_w, e_{vw})$ incorporating bond stereo, conjugation, and aromaticity. Top performer for steric-dependent thermodynamic properties.
+2. **Graph Isomorphism Network (GIN)**: Employs sum-aggregation with multi-layer perceptrons approximating the 1-Weisfeiler-Lehman topological graph isomorphism test.
+3. **Graph Convolutional Networks (GCN)**: First-order localized spectral convolutions with symmetric normalized Laplacian aggregations.
+4. **Graph Attention Networks (GAT)**: LeakyReLU-parameterized multi-head attention over bonded neighborhoods.
+5. **Random Forest Baselines**: 500-estimator ensembles trained on 2048-bit ECFP4 (Morgan radius 2) fingerprints.
+
+---
+
+## 🔗 Project Resources
+
+- **Full Application Repository**: [https://github.com/VSRAJHAVEL/BioGraph-AI](https://github.com/VSRAJHAVEL/BioGraph-AI)
+- **Interactive Web App**: Includes 3D Mol* Conformer Studio, ADMET Spider Radar, MMP Bioisosteric Optimization, PCA Chemical Space Explorer, and GNNExplainer Attribution.
+- **License**: MIT License (Copyright 2026 RAJHAVEL V S)