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Polypharmacology Within the Full Kinome: a Machine Learning Approach
Derek Jones1, Jeevith Bopaiah1, Fatemah Alghamedy1
1University of Kentucky, Lexington, KY, USA.
Summary
Machine learning models can predict how drugs interact with protein kinases, improving cancer therapy development. This approach offers a significant success rate increase over traditional molecular docking methods.
Area of Science:
- Biochemistry
- Pharmacology
- Computational Biology
Background:
- Protein kinases regulate essential cellular functions and signal transduction.
- Kinase deregulation is linked to numerous diseases, particularly cancer.
- Kinase inhibitors represent a major class of modern cancer therapeutics.
Purpose of the Study:
- To develop a machine learning model for investigating polypharmacology across the entire kinome.
- To enhance the prediction of drug-kinase interactions for safer and more effective cancer therapies.
Main Methods:
- Evaluation of diverse feature sets for machine learning model performance.
- Comparison of machine learning model predictions against molecular docking scores.
Main Results:
- Machine learning models outperformed molecular docking across all evaluated feature sets.
- A nearly 60% increase in success rate for identifying binding compounds was achieved using the developed model compared to molecular docking.
Conclusions:
- Machine learning provides a powerful computational tool for exploring kinome-wide drug interactions.
- The developed model offers improved accuracy and success rates for identifying potential kinase-binding compounds, aiding drug discovery.
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