Update README.md

README.md

@@ -1,23 +1,137 @@
 ---
 license: apache-2.0
+tags:
+- molecular-toxicity
+- drug-discovery
+- graph-neural-network
+- cheminformatics
+- tox21
+- interpretability
+- gineconv
+language:
+- en
 ---
----
-license: apache-2.0
----
 
-# MolScreen
+# 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. Accepted to **NeurIPS 2026 AIDaR Workshop** (Paris, December 2026).
+
+## 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)
+- **BIBM 2026 AIPBDA Workshop** — SUBMITTED (Paper ID S49203)
+- **Harvard HSRC 2026** — ACCEPTED (virtual, October 17-18 2026)
+- **Regeneron STS 2027** — SUBMITTED (November 2026)
+- AAAI-27 AISI Track — Submitted (Submission #598)
+
+## GitHub
+
+[github.com/vgoradia/mol-screen](https://github.com/vgoradia/mol-screen)
 
-Graph neural network for early-stage molecular toxicity screening.
+## Citation
 
-Trained on the Tox21 dataset (7,823 compounds, 12 toxicity endpoints).
-Test mean ROC-AUC: 0.8525.
+@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}
+}
 
-**Live app:** https://mol-screen-32jrti5gpuajmd8wse5u3f.streamlit.app
-**GitHub:** https://github.com/vgoradia/mol-screen
+## License
 
-## Usage
+Apache 2.0
 
-Load with PyTorch:
+## Contact
 
-    model = MolScreenGNN()
-    model.load_state_dict(torch.load('molscreen_best.pt'))
+Veer Goradia · vgoradia07@gmail.com