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README.md

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+---
+license: mit
+tags:
+  - protein
+  - binding-affinity
+  - deep-learning
+  - esm
+  - pytorch
+language:
+  - en
+---
+
+# 🧬 Protein Binding Affinity Predictor
+
+Dual-head model for predicting protein-protein binding affinity (ΔG) and mutation effects (ΔΔG).
+
+## Model Performance
+
+| Metric | Validation Score |
+|--------|-----------------|
+| dG Pearson | 0.51 |
+| ddG Pearson | 0.70 |
+| Sum PCC | 1.21 |
+
+## Architecture
+
+- **Backbone**: ESM-600M (frozen embeddings)
+- **Pooling**: Sliced-Wasserstein Embedding (SWE)
+- **Heads**: Dual-head (dG + ddG)
+- **Input**: Protein sequences (1153-dim = 1152 ESM + 1 mutation channel)
+
+## Usage
+
+```python
+from huggingface_hub import hf_hub_download
+import torch
+
+# Download checkpoint
+ckpt = hf_hub_download(repo_id="supanthadey1/protein-binding-affinity", filename="best_model_checkpoint.pt")
+checkpoint = torch.load(ckpt, map_location='cpu')
+model.load_state_dict(checkpoint['model_state_dict'])
+```
+
+## Predictions
+
+- **ΔG (kcal/mol)**: Binding free energy. More negative = stronger binding.
+- **ΔΔG (kcal/mol)**: Mutation effect. Negative = stabilizing, Positive = destabilizing.
+
+## Training Data
+
+Trained on multiple datasets including SKEMPI, BindingGym, PDBbind, and others.
+
+## Citation
+
+```
+[Citation coming soon]
+```