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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]
```
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