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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: 1.01
            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

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
Portfolio: vsrajhavel.github.io | LinkedIn: in/rajhavelvs | GitHub: @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

pip install torch torch-geometric rdkit huggingface_hub

Download Pretrained Weights

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

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.
Graph Isomorphism Network (GIN): Employs sum-aggregation with multi-layer perceptrons approximating the 1-Weisfeiler-Lehman topological graph isomorphism test.
Graph Convolutional Networks (GCN): First-order localized spectral convolutions with symmetric normalized Laplacian aggregations.
Graph Attention Networks (GAT): LeakyReLU-parameterized multi-head attention over bonded neighborhoods.
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
Interactive Web App: Includes 3D Mol* Conformer Studio, ADMET Spider Radar, MMP Bioisosteric Optimization, PCA Chemical Space Explorer, and GNNExplainer Attribution.