| 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] | |
| ``` | |