In Silico Identification of Potential PD-L1 and VISTA Inhibitors in Ovarian Cancer: A Computational Approach Combining Virtual Screening and Molecular Dynamics Simulations

Introduction Immune checkpoint blockade targeting PD-1/PD-L1 has revolutionized cancer treatment; however, resistance remains a major clinical challenge. V-domain Immunoglobulin Suppressor of T cell Activation (VISTA), a B7 family member with high expression in tumor-infiltrating lymphocytes of ovarian cancer, has emerged as a promising alternative target for immunotherapeutic intervention. Materials and Methods We performed in silico screening of 9,397 DrugBank compounds against PD-L1 and VISTA using AutoDock Vina. The top candidates based on docking scores were assessed through 100 ns molecular dynamics simulations, and binding free energies were calculated via MM-PBSA. Results DB15637, DB12867, and DB06744 showed the strongest PD-L1 binding affinities (−7.33 to −7.87 kcal/mol) with average RMSD values of 8.89 Å, 8.94 Å, and 7.57 Å, respectively. DB00321 exhibited the highest affinity for VISTA (−7.31 kcal/mol) with an RMSD of 6.18 Å, maintaining stable interactions with key residues throughout the simulation. Discussion The identified compounds demonstrated favorable docking scores, dynamic stability, and binding free energies, suggesting their potential as PD-L1 and VISTA inhibitors. Dual checkpoint targeting could enhance antitumor immune responses in ovarian cancer, where both proteins contribute to immune evasion. Conclusion This in silico study identified promising candidates for PD-L1 and VISTA inhibition. These findings provide a computational basis for further experimental validation to confirm their therapeutic potential in the treatment of ovarian cancer.