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Multi-omics pan-cancer profiling of CDK2 and <i>in silico</i> identification of plant-derived inhibitors using machine learning approaches

Md. Ahad Ali, Hillol Sarker, Tania Khan, Humaira Sheikh, Ahmed Saif, F. Farid, Sadia Afrin, Most. Asha Khatun, Neeraj Kumar

2025RSC Advances7 citationsDOIOpen Access PDF

Abstract

, respectively. These lead phytocompounds exhibited high potency, excellent pharmacokinetic properties, and minimal predicted toxicity as compared with the control inhibitor of CDK2. The binding stability of the protein-ligand complexes was confirmed by dynamic simulations along with MM-GBSA calculations, with the results supporting our previously reported affinity score. Therefore, these phytocompounds could be potential CDK2 inhibitors, warranting exploration in future cancer research. Furthermore, additional experimental and clinical validations are required to confirm the efficacy and efficiency of these potential lead compounds.

Topics & Concepts

ADMEComputational biologyDocking (animal)Cyclin-dependent kinase 2In silicoCombination therapyBinding affinitiesMachine learningChemistryKinaseQuantitative structure–activity relationshipArtificial intelligenceBiologyCancer researchIdentification (biology)PharmacologyProfiling (computer programming)Cancer therapyBiochemistryCell cycleCancer cellBioinformatics and Genomic NetworksGenetics, Bioinformatics, and Biomedical ResearchGene expression and cancer classification