README.md

metadata

title: AntioxFP — GNN Antioxidant Activity Predictor
emoji: 🧪
colorFrom: blue
colorTo: green
sdk: docker
app_port: 7860
pinned: false
license: mit
short_description: Predict antioxidant activity (DPPH pIC50) with GNN

AntioxFP 🧪

GNN-Based Antioxidant Activity Predictor

Predict DPPH• radical scavenging activity (pIC₅₀) from molecular SMILES strings using a 30-model AttentiveFP ensemble, with GNNExplainer atom-level importance maps to reveal which structural features drive the prediction.

Features

• 30-model ensemble (3 random seeds × 10-fold CV): AttentiveFP + Mordred hybrid
• Prediction uncertainty: mean ± std across all 30 fold models
• Atom importance maps: GNNExplainer 2D heatmaps, pharmacophore-level analysis
• Exceeds benchmark: R² = 0.785 vs. descriptor-based reference (R² = 0.78)
• Applicability domain: ~98% coverage of DPPH antioxidant chemical space

How to Use

1. Enter a SMILES string in the input box (or click an example)
2. Optionally enable GNNExplainer (adds ~10–20 s) for atom importance maps
3. Click Predict Activity

Interpretation

pIC₅₀	Activity Level	Estimated IC₅₀
≥ 5.0	🟢 High	≤ 10 µM
4.5–5.0	🟡 Moderate–High	10–32 µM
4.0–4.5	🟠 Moderate	32–100 µM
< 4.0	🔴 Low	> 100 µM

Scientific Background

The model was trained on 1,911 DPPH antioxidant compounds from the Antioxidant Database (AODB) curated by Ghironi et al. (2025), using the same 80:20 random split for direct head-to-head comparison.

Architecture: AttentiveFP (Xiong et al., J. Med. Chem. 2020) with

• 2 graph attention layers, hidden dim = 200, 2 readout timesteps
• Trained with Adam optimizer, ReduceLROnPlateau scheduler
• Early stopping on validation R²

Reference paper: Graph Neural Network Models for Predicting the Antioxidant Activity of Chemical Compounds: Scaffold-Based Evaluation and Interpretability Analysis, 2025.

Pharmacophore Insights

The GNNExplainer atom maps confirm that the model has learned chemically meaningful SAR:

• Catechol B ring (3',4'-OH): primary HAT pharmacophore → highest atom importance
• C-ring 3-OH (flavonols like quercetin): secondary importance
• Conjugated chromone/carbonyl: supports radical delocalization
• Stilbene vinyl bridge: SET pathway marker in resveratrol-type compounds

Citation

If you use AntioxFP in your research, please cite:

Graph Neural Network Models for Predicting the Antioxidant Activity of Chemical Compounds, 2025.

License

MIT License. Models and code available upon paper acceptance.