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README.md

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----
-title: AntioxFP
-emoji: 🏢
-colorFrom: yellow
-colorTo: red
-sdk: gradio
-sdk_version: 6.13.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: AntioxFP — GNN Antioxidant Activity Predictor
+emoji: 🧪
+colorFrom: blue
+colorTo: green
+sdk: gradio
+sdk_version: 5.49.1
+app_file: app.py
+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)](https://food.shujucloud.com/) 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.