KinCSM: Using graph-based signatures to predict small molecule CDK2 inhibitors.

ABSTRACT: Protein phosphorylation acts as an essential on/off switch in many cellular signaling pathways. This has led to ongoing interest in targeting kinases for therapeutic intervention. Computer-aided drug discovery has been proven a useful and cost-effective approach for facilitating prioritization and enrichment of screening libraries, but limited effort has been devoted providing insights on what makes a potent kinase inhibitor. To fill this gap, here we developed kinCSM, an integrative computational tool capable of accurately identifying potent cyclin-dependent kinase 2 (CDK2) inhibitors, quantitatively predicting CDK2 ligand-kinase inhibition constants (pK_i ) and classifying different types of inhibitors based on their favorable binding modes. kinCSM predictive models were built

SUBMITTER: Zhou Y 

PROVIDER: S-EPMC9597374 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature