|
|
--- |
|
|
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 |
|
|
|
|
|
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. |
|
|
|
