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---
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.
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