Abstract
Summary Kinases are one of the most important classes of drug targets for therapeutic use. Algorithms that can accurately predict the drug-kinase inhibitor constant (pKi) of kinases can considerably accelerate the drug discovery process. In this study, we have developed computational models, leveraging machine learning techniques, to predict ligand-kinase (pKi) values. Kinase-ligand inhibitor constant (Ki) data was retrieved from Drug Target Commons (DTC) and Metz databases. Machine learning models were developed based on structural and physicochemical features of the protein and, topological pharmacophore atomic triplets fingerprints of the ligands. Three machine learning models [random forest (RF), extreme gradient boosting (XGBoost) and artificial neural network (ANN)] were tested for model development. RF model was finally selected based on the evaluation metrics on test datasets and used for web implementation.
Availability GitHub: https://github.com/sirimullalab/KinasepKipred, Docker: sirimullalab/kinasepkipred
Implementation https://drugdiscovery.utep.edu/pki
Contact ssirimulla{at}utep.edu
Supplementary information Supplementary data are available Bioinformatics online.
