KinasepKipred: A Predictive Model for Estimating Ligand-Kinase Inhibitor Constant (pKi)

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 (RFR), extreme gradient boosting (XGBoost) and artificial neural network (ANN)] were tested for model development. The performance of our models were evaluated using several metrics with 95% confidence interval. RFR model was finally selected based on the evaluation metrics on test datasets and used for web implementation. The best and selected model achieved a Pearson correlation coefficient (R) of 0.887 (0.881, 0.893), root-mean-square error (RMSE) of 0.475 (0.465, 0.486), Concordance index (Con. Index) of 0.854 (0.851, 0.858), and an area under the curve of receiver operating characteristic curve (AUC-ROC) of 0.957 (0.954, 0.960) during the internal 5-fold cross validation. Availability GitHub: https://github.com/sirimullalab/KinasepKipred, Docker: sirimullalab/kinasepkipred Implementation https://drugdiscovery.utep.edu/pki/ Graphical TOC Entry

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