| --- |
| license: apache-2.0 |
| tags: |
| - molecular-toxicity |
| - drug-discovery |
| - graph-neural-network |
| - cheminformatics |
| - tox21 |
| - interpretability |
| - gineconv |
| language: |
| - en |
| --- |
| |
| # MolScreen: GNN for Molecular Toxicity Screening |
|
|
| **MolScreen** is a Graph Isomorphism Network with Edge features (GINEConv) trained on the Tox21 dataset for multi-task molecular toxicity prediction. It combines GNN-based predictions with gradient-based atom attribution and LLM-generated triage notes to make toxicity screening interpretable and actionable. |
|
|
| Developed by Veer Goradia. |
|
|
| ## Model Description |
|
|
| Drug toxicity is one of the leading causes of late-stage clinical trial failure, costing billions in wasted development. MolScreen addresses two key gaps in existing approaches: most models give a probability but no explanation, and none integrate a human-readable summary for non-expert decision makers. |
|
|
| **Architecture:** |
| - GINEConv backbone — uses edge features (bond type, aromaticity, conjugation) that plain GCN/GIN discard |
| - Multi-task classification across 12 Tox21 endpoints simultaneously |
| - Masked BCE loss for Tox21's missing labels (5,800–7,200 valid labels per task) |
| - Gradient-based atom attribution: highlights which molecular substructures drove the prediction |
| - LLM triage layer: generates a 3-sentence actionable summary (SMILES + flagged probabilities + top-3 attributed atoms per task) |
|
|
| **Total parameters:** ~374,000 |
| **Inference time:** ~0.7 seconds end-to-end (excluding LLM API call) |
| **Training time:** ~6 minutes on laptop CPU (60 epochs) |
|
|
| ## Performance |
|
|
| ### Random Split (10-seed validation) |
| | Metric | Value | |
| |--------|-------| |
| | Mean ROC-AUC | 0.8442 ± 0.0073 | |
|
|
| ### Scaffold Split (10-seed validation) |
| | Metric | Value | |
| |--------|-------| |
| | Mean ROC-AUC | 0.7716 ± 0.0036 | |
|
|
| ### Baseline Comparison (Scaffold Split) |
| | Model | ROC-AUC | |
| |-------|---------| |
| | **MolScreen (GINEConv)** | **0.7716** | |
| | Plain GCN | 0.7481 | |
| | Random Forest (Morgan FP) | 0.7465 | |
| | XGBoost (Morgan FP) | 0.7378 | |
| | Plain GIN | 0.7249 | |
| | Chen et al. SSL-GCN (2021) | 0.7570 | |
|
|
| MolScreen beats the best non-pretrained baseline (GCN) by 2.4 points. The GINEConv edge-awareness advantage over plain GIN (+4.7 points) confirms that bond-level features matter for generalization. |
|
|
| ### Per-Task ROC-AUC (Random Split, representative run) |
| | Endpoint | AUC | |
| |----------|-----| |
| | NR-AR | 0.8518 | |
| | NR-AR-LBD | 0.9119 | |
| | NR-AhR | 0.8938 | |
| | NR-Aromatase | 0.8788 | |
| | NR-ER | 0.7486 | |
| | NR-ER-LBD | 0.8400 | |
| | NR-PPAR-gamma | 0.8918 | |
| | SR-ARE | 0.8125 | |
| | SR-ATAD5 | 0.8762 | |
| | SR-HSE | 0.7374 | |
| | SR-MMP | 0.9156 | |
| | SR-p53 | 0.8713 | |
|
|
| ## Training Data |
|
|
| - **Dataset:** Tox21 (7,823 valid compounds, 12 toxicity endpoints) |
| - **Assay categories:** Nuclear receptor panel (NR-*) and stress response panel (SR-*) |
| - **Class imbalance:** 2.6%–16% positive rate per task |
| - **Splits:** Random split (80/10/10) and scaffold split (chemotype-based, harder generalization test) |
|
|
| ## Intended Use |
|
|
| - Early-stage drug candidate toxicity screening |
| - Prioritizing which compounds advance to lab testing |
| - Interpretability analysis via atom-level attribution maps |
| - Research on GNN-based molecular property prediction |
|
|
| ## How to Use |
|
|
| ```python |
| import torch |
| from huggingface_hub import hf_hub_download |
| |
| # Load model from HuggingFace |
| model_path = hf_hub_download(repo_id="vgoradia/MolScreen", filename="molscreen_best.pt") |
| model = torch.load(model_path, map_location='cpu') |
| model.eval() |
| |
| # Or try the live Streamlit app — no code needed |
| ``` |
|
|
| ## Live Demo |
|
|
| Try the interactive app (draw a molecule, get toxicity predictions + atom attribution): |
| [MolScreen Streamlit App](https://mol-screen-32jrti5gpuajmd8wse5u3f.streamlit.app) |
|
|
| ## Publications & Acceptances |
|
|
| - **NeurIPS 2026 AIDaR Workshop** — ACCEPTED (poster, Paris, December 12 2026) |
| - **IEEE BIBM 2026 AIPBDA Workshop** — SUBMITTED (Paper ID S49203) |
| - **Regeneron STS 2027** — In Progress (November 2026) |
| - **AAAI-27 AISI Track** — Submitted (Submission #598) |
|
|
| ## GitHub |
|
|
| [github.com/vgoradia/mol-screen](https://github.com/vgoradia/mol-screen) |
|
|
| ## Citation |
|
|
| @misc{goradia2026molscreen, |
| title={MolScreen: Interpretable Multi-Task Molecular Toxicity Screening |
| with Graph Neural Networks and LLM Triage}, |
| author={Goradia, Veer}, |
| year={2026}, |
| note={NeurIPS 2026 AIDaR Workshop} |
| } |
|
|
| ## License |
|
|
| Apache 2.0 |
|
|
| ## Contact |
|
|
| Veer Goradia · vgoradia07@gmail.com |
