From in silico and machine learning model to validation: discovery of novel amide-functionalized imidazopyridines as PIM-1 kinase inhibitors using an integrated ligand and structure-based hopping approach

Abstract

Pro-viral integration sites for Moloney murine leukaemia virus (PIM) kinases are the members of serine/threonine kinase family, that elevate cell division and inhibit apoptosis, thus making them as an attractive anticancer target. In this study, a virtual screening strategy based on a pharmacophore model was used to unearth imidazo[1,2-a]pyridin-2-yl phenyl benzamides as potential PIM1 inhibitors. A Python-based design workflow generated 27 candidate molecules, whose inhibitory activities were predicted using an in-house linear regression machine learning model. Selected compounds were synthesized, characterized, and subjected to cytotoxicity studies across multiple cancer cell lines, alongside assessments of PIM1 inhibition and apoptosis induction. Among the tested molecules, compound 6h demonstrated the most promising antiproliferation activity in HT-29, PC3, and A549 cells, inducing apoptosis in HT-29 cells. It inhibited PIM1 kinase activity by 34.5% ± 3.4% at 10 µM and exhibited favorable in silico ADME and drug-likeness profiles, including high predicted absorption and absence of hepatotoxicity. These findings establish 6h as a promising lead scaffold for the development of PIM1-1 targeted anticancer agents. The integration of pharmacophore modeling, machine learning prediction, and experimental validation highlights a viable strategy for accelerating kinase inhibitor discovery.