| --- |
| license: mit |
| tags: |
| - drug-discovery |
| - protein-ligand-binding |
| - binding-kinetics |
| - deep-learning |
| - computational-biology |
| - bioinformatics |
| library_name: pytorch |
| datasets: |
| - kineticX |
| --- |
| |
| # BiCoA-Net: Bidirectional Co-Attention Network |
|
|
| ## Model Description |
|
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| BiCoA-Net predicts protein-ligand dissociation rate constants (k_off) using bidirectional co-attention mechanisms between protein and ligand representations. |
| |
| **Key Features:** |
| - Predicts binding kinetics (k_off) for drug-target interactions |
| - Uses ESM-2 protein embeddings + MolFormer ligand embeddings |
| - Bidirectional co-attention fusion mechanism |
| - Trained on curated KineticX datasets |
|
|
|
|
| ## Training Details |
|
|
| - Optimizer: AdamW |
| - Learning Rate: 1e-4 |
| - Batch Size: 32 |
| - Epochs: 100 |
| - Loss Function: MSE on log(k_off) |
| |
| |
| |
| ## License |
| |
| MIT License - Free for academic and commercial use. |
| |
| ## Contact |
| |
| For questions or issues, please open an issue on the GitHub repository or contact the authors. |
| |
