ArticleMolecular diversity2026
Harnessing machine learning, docking and molecular dynamics for the virtual screening of compounds as CDK4/6 dual inhibitors.
Article in Molecular diversity, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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6 authors.
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Abstract
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 R
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