SoluBat: A Bidirectional Mamba Framework for High-Throughput Protein Solubility Prediction in Bioprocess Optimization
SoluBat is a sophisticated hybrid model designed for precise protein solubility prediction, incorporating the Mamba model.
- Bidirectional Mamba model boosts accuracy in protein solubility prediction.
- Dynamic gating integrates multimodal features with high efficiency.
- Near-linear complexity reduces GPU usage compared to Transformers.
- Built-in residue-level attribution enhances biological interpretability.
- Extensive benchmarking confirms strong generalization and industrial applicability.
Please make sure you have installed Anaconda3 or Miniconda3.
conda env create -f environment.yaml
conda activate SoluBat| Parameter | Description |
|---|---|
| data_root | Root directory for raw data |
| num_folds | Number of cross-validation splits |
| fold_idx | Fold index (-1 for all, 0-6 for single) |
| batch_size | Training batch size |
| lr | Initial learning rate |
| max_epochs | Maximum training epochs |
python scripts/train.pyContributions and suggestions from the community are welcome! If you find a bug or have an improvement suggestion, please submit an issue or a pull request.
This project is licensed under the MIT License.