Computational identification of novel inhibitors targeting vascular endothelial growth factor receptor 2-mediated angiogenesis in cervical carcinoma

Natural products remain an invaluable source of structurally diverse scaffolds for drug discovery, including potential inhibitors of Vascular Endothelial Growth Factor Receptor 2 (VEGFR2), a critical target in angiogenesis and cancer. In this study, a large-scale structure-based virtual screening pipeline was applied to identify promising VEGFR2 ligands from the COCONUT natural product database. The initial dataset of 683,307 compounds was filtered for drug-likeness using the OMEGA Classic Lead-Like Filter, resulting in 346,860 lead-like compounds. Following conformer generation, rigid exhaustive docking was performed against VEGFR2 using Fast Rigid Exhaustive Docking (FRED) (OpenEye Scientific Software), with compounds ranked by the Chemgauss4 scoring function. Four top-scoring hits were identified: CNP0048502, CNP0599152, CNP0511796, and CNP0340962, with Chemgauss4 scores of -20.35, -19.41, -19.01, and -18.97, respectively. Detailed interaction analysis revealed that these compounds formed stable hydrogen bonds and hydrophobic contacts with key VEGFR2 residues, suggesting strong predicted binding affinity and shape complementarity. This integrated virtual screening approach prioritized natural product derived ligands with favorable drug-like properties as putative VEGFR2 inhibitory candidates. These findings warrant further in vitro validation to assess their therapeutic potential in anti-angiogenic drug development.