Integrated Analysis, Machine Learning, Molecular Docking and Dynamics of CDK1 Inhibitors in Epithelial Ovarian Cancer: A Multifaceted Approach Towards Targeted Therapy

Epithelial ovarian cancer (EOC) remains one of the deadliest gynecologic malignancies due to delayed diagnosis and resistance to conventional therapies. Identifying pivotal molecular targets is crucial for advancing therapeutic strategies. 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 its regulator WEE1. Naringin, a bioactive flavonoid, demonstrated high-affinity binding, stable complex formation, and minimal toxicity based on ADMET predictions. It effectively targeted both CDK1 and WEE1, inducing structural flexibility conducive to inhibitory function. Our findings establish CDK1 as a high-confidence therapeutic target in EOC and present Naringin as a promising dual-target inhibitor with translational potential. This study underscores the power of computational-experimental integration in accelerating oncology drug discovery.