takshan / aikpro

AiKPro: deep learning model for kinome-wide bioactivity profiling using structure-based sequence alignments and molecular 3D conformer ensemble descriptors

AiKPro

Park, H., Hong, S., Lee, M. et al. AiKPro: deep learning model for kinome-wide bioactivity profiling using structure-based sequence alignments and molecular 3D conformer ensemble descriptors. Sci Rep 13, 10268 (2023). https://doi.org/10.1038/s41598-023-37456-8

AiKPro is an advanced deep learning model designed to predict the kinase profile of a drug using state-of-the-art artificial intelligence techniques. By leveraging large-scale datasets and sophisticated neural networks, AiKPro offers accurate and reliable predictions of kinase interactions, facilitating drug development and optimization processes.

Features

• Predicts the kinase profile of a given drug compound.
• Utilizes deep learning algorithms for precise predictions.
• Integrates large-scale kinase and compound datasets for robust model training.
• Offers user-friendly APIs for seamless integration into existing workflows.
• Provides detailed documentation and examples for easy usage and implementation.

Contributing

Contributions to AiKPro are welcome! Please refer to the CONTRIBUTING.md file for guidelines on how to contribute.

License

AiKPro is licensed under the MIT License. See the LICENSE file for details.

Contact

For any questions, feedback, or inquiries, please contact [[email protected]].