Machine Learning-Based Virtual Screening for the Identification of Novel CDK-9 Inhibitors
Abstract
Cyclin-dependent kinase 9 (CDK9) is a key regulator of transcriptional elongation and DNA repair, supporting cancer cell survival by sustaining the expression of oncogenes and anti-apoptotic proteins. Its overexpression in multiple malignancies makes it an attractive target for anticancer therapy. Here, we report a machine learning (ML) based approach to identify novel CDK9 inhibitors. Through systematic data collection and preprocessing, seventy predictive models were developed using five algorithms, two classification settings, and seven molecular representations. The best-performing model was employed to guide a virtual screening (VS) campaign, resulting in the identification of 14 compounds promising for their potential inhibitory effect. Upon enzymatic assays, two molecules with inhibitory activity in the low micromolar range were selected as promising candidates and further tested in three cancer cell lines with distinct genetic backgrounds. These experiments led to the identification of a novel compound exhibiting interesting therapeutic potential, both as a single agent and in combination with Camptothecin (CPT), revealing varying response profiles across the tested cell lines. These results illustrate the power of integrating ML within anticancer drug discovery pipelines and represent a valuable starting point for the development of novel CDK9 inhibitors.
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