README.md

metadata

language: en
license: mit
library_name: pytorch
tags:
  - chemistry
  - toxicity-prediction
  - graph-neural-network
  - drug-discovery
  - tox21
  - multi-task-learning
  - uncertainty-quantification
pipeline_tag: feature-extraction
datasets:
  - tox21
metrics:
  - name: CV AUC Mean
    type: roc_auc
    value: 0.7856
  - name: CV AUC Std
    type: roc_auc_std
    value: 0.0394
  - name: Number of Tasks
    type: count
    value: 10
model-index:
  - name: ToxiPredict GNN
    results:
      - task:
          type: toxicity-prediction
          name: Multi-Task Toxicity Prediction
        dataset:
          type: tox21
          name: Tox21
          config: 12-bioassay
        metrics:
          - type: roc_auc
            value: 0.7856
            name: 5-Fold CV Mean AUC
          - type: roc_auc_std
            value: 0.0394
            name: 5-Fold CV AUC Standard Deviation

ToxiPredict — Multi-Task GNN for Toxicophore Prediction

Uncertainty-aware multi-task graph neural network trained on the Tox21 dataset for predicting toxicity across 10 biological endpoints, with 5-fold cross-validated performance.

Model Description

Property	Value
Architecture	MultiTaskGNN_ResGATv2_JK_VN
Input	Molecular graphs (SMILES → 45-dim node, 11-dim edge features)
Output	10 binary toxicity predictions + uncertainty weights
Parameters	12 learnable homoscedastic uncertainty log-variance parameters
Training Data	Tox21 (6264 training compounds after scaffold split)
Validation	5-fold Bemis-Murcko scaffold cross-validation
Framework	PyTorch 2.10 + PyTorch Geometric 2.6

Performance

5-Fold Cross-Validation: 0.7856 ± 0.0394 Mean AUC

The model was evaluated using Bemis-Murcko scaffold split, ensuring that structurally similar molecules are grouped in the same fold. This provides a realistic estimate of generalization to novel chemical scaffolds.

Task	Type
NR-AR	Nuclear Receptor
NR-AhR	Nuclear Receptor
NR-Aromatase	Nuclear Receptor
NR-ER	Nuclear Receptor
NR-PPAR-gamma	Nuclear Receptor
SR-ARE	Stress Response
SR-ATAD5	Stress Response
SR-HSE	Stress Response
SR-MMP	Stress Response
SR-p53	Stress Response

Architecture Details

The model extends standard GAT with three key innovations:

1. Residual GATv2 Convolutions: Two-layer GATv2 with residual connections and 4 attention heads per layer, providing dynamic attention mechanisms that adapt to molecular substructures.
2. JumpingKnowledge (JK) Aggregation: Concatenates intermediate layer representations before prediction, preserving both local and global structural information.
3. Virtual Node: A learned virtual node connected to all atoms enables global molecular context propagation across the graph.
4. Homoscedastic Uncertainty Weighting: Learnable per-task log-variance parameters dynamically balance gradient contributions during multi-task training.

Usage

import torch
from safetensors.torch import load_file
from huggingface_hub import hf_hub_download

# Download model
model_path = hf_hub_download(
    repo_id="Arko007/toxipredict-gnn-models",
    filename="model.safetensors"
)
state_dict = load_file(model_path)

# Load config
config_path = hf_hub_download(
    repo_id="Arko007/toxipredict-gnn-models",
    filename="model_config.json"
)
import json
with open(config_path) as f:
    config = json.load(f)

# Initialize model with same architecture
model = MultiTaskGNN_ResGATv2_JK_VN(
    node_dim=config["node_dim"],
    edge_dim=config["edge_dim"],
    hidden_dim=config["hidden_dim"],
    num_tasks=config["num_tasks"],
    dropout=config["dropout"]
)
model.load_state_dict(state_dict)
model.eval()

Training Details

• Optimizer: Adam (lr=1e-3, weight_decay=1e-5)
• Batch Size: 64
• Max Epochs: 200 (early stopping patience=20)
• Loss: Homoscedastic uncertainty-weighted binary cross-entropy
• Hardware: NVIDIA T4 GPU (Kaggle)
• Training Time: ~12 minutes per run

References

• Tox21 Challenge: https://tripod.nih.gov/tox21/challenge/
• GATv2: Brody et al., ICLR 2022
• JumpingKnowledge: Xu et al., ICML 2018
• Homoscedastic Uncertainty: Kendall et al., NeurIPS 2017

License

MIT