---
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