Computational Evaluation of Chromen-2-one-Oxadiazole Derivatives as Next-Generation Non-Steroidal Androgen Receptor Antagonists

Castration-resistant prostate cancer (CRPC) develops mainly because mutation occurs in the androgen receptor (AR) which reduce the effectiveness of existing anti-androgen drugs. This creates a need for new AR antagonists that can remain active even in mutated receptors. In this study, a complete computational approach combining machine learning (ML)-based virtual screening, molecular docking, Density function theory (DFT) calculations, drug likeness prediction, molecular dynamic simulations and MM/PBSA analysis was used to identify chromen-2-one-oxadiazole derivatives as potential non-steroidal AR antagonists. A random forest model trained on BindingDB data predicted the activity of designed compounds after screening. Among them, VP1-071 and VP1-095 showed best results, with strong docking scores against wild type-AR (-10.8 and -10.4 kcal/mol). Both compounds also maintained good binding with resistance mutants T877A and W741L. DFT calculations indicated stable electronic properties, while 200ns molecular dynamic simulations confirmed stable protein-ligand complexes. MM/PBSA analysis further supported strong binding interactions.