Harnessing machine learning, docking and molecular dynamics for the virtual screening of compounds as CDK4/6 dual inhibitors.

Cyclin-dependent kinases 4 and 6 (CDK4/6) are pivotal regulators of the G1-to-S phase transition, and their dysregulation is a hallmark of numerous malignancies. Despite the clinical success of existing CDK4/6 inhibitors, there remains a persistent need for chemically diverse scaffolds with potent dual-target affinity. In this study, we developed and implemented a virtual screening workflow that synergistically integrates ligand-based machine learning with structure-based molecular docking. By benchmarking multiple ML algorithms against curated ChEMBL datasets (265 CDK4 inhibitors and 402 CDK6 inhibitors), a Bayesian Ridge regressor utilizing ECFP4 fingerprints was identified as the most predictive model, achieving cross-validated R2 values of 0.731 ± 0.022 for CDK4 and 0.721 ± 0.070 for CDK6. This optimized ML filter was deployed to prioritize a 22,823-compound library, followed by rigorous dual-target docking refinement. This strategy prioritized three candidate hits for biochemical evaluation, among which HY-18,623 showed potent dual inhibitory activity, with IC50 values of 3.5 nM against CDK4 and 17.4 nM against CDK6. Extensive 200-ns molecular dynamics simulations and binding free energy analyses elucidated that HY-18,623 achieves high-affinity binding through persistent hydrogen bonds with hinge residues Val96 (CDK4) and Val101 (CDK6). These findings demonstrate that our integrated computational funnel is a highly efficient tool for discovering potent kinase inhibitors and position HY-18,623 as a promising lead candidate for further therapeutic development in oncology.