Integrated Analysis, Machine Learning, Molecular Docking and Dynamics of CDK1 Inhibitors in Epithelial Ovarian...

Epithelial ovarian cancer (EOC) remains one of the deadliest gynecologic malignancies, largely due to late diagnosis and treatment resistance. The main objective of this study is to identify and validate CDK1 as a high-confidence therapeutic target in EOC and to assess the dual-target inhibitory potential of the natural compound Naringin against both CDK1 and its regulator WEE1. This study employed an integrative pipeline combining transcriptomic profiling, protein-protein interaction network analysis, machine learning, and molecular simulations to identify key oncogenic regulators in EOC. CDK1 emerged as a central hub gene, exhibiting strong association with poor prognosis and signaling convergence. CDK1 overexpression correlated with adverse survival outcomes and robust involvement in critical oncogenic pathways. Molecular docking and dynamics simulations assessed the binding efficacy of seven compounds with CDK1 and WEE1, with Naringin showing high-affinity binding, stable complex formation, and minimal predicted toxicity. This study underscores the power of computational-experimental integration in accelerating oncology drug discovery, providing visual and quantitative evidence that systematically connect the study's aim to its findings.