Molecular modeling of AI-assisted structural modification of β-caryophyllene toward improved binding to xanthine oxidase: DFT and molecular dynamics studies

Abstract

β-Caryophyllene (BCP) is a naturally occurring sesquiterpene that has been reported to exhibit measurable inhibitory activity against xanthine oxidase (XO); however, its potency remains inferior to that of allopurinol. To overcome this limitation, artificial intelligence-assisted structural modification was employed to optimize the BCP scaffold for enhanced XO inhibition. A series of artificial intelligence (AI)-generated BCP derivatives were systematically evaluated using density functional theory (DFT), molecular docking, molecular dynamics (MD) simulations, and binding free energy calculations. All modified derivatives demonstrated improved binding affinity toward XO compared to native BCP and oxypurinol, indicating the effectiveness of AI-assisted scaffold optimization. Among these, the CD1 and CD4 derivatives exhibited the most favorable interaction profiles. DFT analysis revealed that structural modifications significantly altered electronic descriptors, including HOMO–LUMO energy gaps and charge distribution, favoring stronger intermolecular interactions. MD simulations over 250 ns confirmed stable complex formation for both CD1-XO and CD4-XO systems, characterized by preserved enzyme compactness, reduced active-site flexibility, stable solvent-accessible surface area, but lower persistent hydrogen bonding relative to oxypurinol. Binding free energy calculations further supported these findings, with MM-PBSA energies of −23.4 kcal/mol for CD4 for CD1 and −21.7 kcal/mol for CD1, compared to −16.3 kcal/mol for oxypurinol. Overall, the integrated computational results demonstrate that AI-assisted modification of the BCP scaffold substantially enhances its interaction stability and binding affinity toward xanthine oxidase, highlighting CD4 as the most promising natural product-derived lead for further experimental investigation.