Integrative Bioinformatics Identification of Baicalein as a Phytochemical Inhibitor of CHEK1 in Serous Ovarian Cancer: A Multi-Stage In Silico Drug Discovery Approach

Background: Serous ovarian cancer (SOC) is the most aggressive subtype of epithelial ovarian cancer, frequently diagnosed at advanced stages with poor prognosis and chemotherapy resistance. Checkpoint kinase 1 (CHEK1), a key regulator of the DNA damage response, is overexpressed in ovarian cancer, making it a promising therapeutic target. No study has systematically evaluated natural compounds as CHEK1 inhibitors in SOC through an integrative transcriptomic and computational framework. Methods: A meta-analysis of three GEO datasets (GSE27651, GSE36668, GSE54388; n = 83) was performed using ImaGEO. DEGs were identified at |log2FC| ≥ 2 and FDR < 0.05. Pathway enrichment, PPI network analysis, virtual screening of 40 phytochemicals against CHEK1 (PDB: 9CE4), 100 ns MD simulations, and ADMET profiling were conducted using established bioinformatics and computational tools. Results: A total of 511 DEGs were identified, with significant dysregulation of apoptosis, DNA repair, and cell cycle pathways. CHEK1 emerged as the central hub gene. Baicalein exhibited the highest binding affinity (−9.334 kcal/mol), surpassing Prexasertib (−7.2 kcal/mol). MD simulations confirmed complex stability, and ADMET profiling demonstrated favorable drug-likeness with zero Lipinski violations. Conclusions: CHEK1 is established as a validated therapeutic target in SOC, and Baicalein is identified as a computationally superior natural lead compound, warranting experimental validation in ovarian cancer models.