GPU-Accelerated Virtual Screening and Molecular Dynamics Simulations for Identification of Novel DPP-4 Inhibitors

Inhibition of dipeptidyl peptidase 4 (DPP-4) is a crucial therapeutic strategy for the management of type 2 diabetes mellitus (T2DM). However, current inhibitors often exhibit unwanted toxicity, underscoring the need to discover novel, selective, and safer alternatives. This study employs an integrated computational pipeline to accelerate the identification of new DPP-4 inhibitor candidates. To that effect, GPU-accelerated molecular docking of 30,699 bioactive PubChem compounds was combined with molecular dynamics (MD) simulations and membrane permeability analyses. A workflow that systematically filters candidates was presented based on the score binding predicted by Uni-Dock. Subsequently, the stability of 32 promising protein-ligand systems was assessed using 100 ns MD trajectories, confirming their stable binding to the DPP-4 active site. Compounds EPZ005687, OSU-03012, and bemcentinib showed higher binding affinity and more favorable interactions within pockets S1, S2, S1', S2', and S2 ' than the FDA-approved reference drugs like alogliptin, based on MM-GBSA calculations. To assess the therapeutic viability of the candidates, their cellular absorption potential was also investigated. Permeability (free energy of transfer profile) and interactions were calculated via Umbrella Sampling and long-time MD across a physiologically relevant enterocyte membrane model. The results revealed that EPZ005687, OSU-03012, and bemcentinib exhibited better permeation characteristics than alogliptin. This combined evidence of high target affinity and enhanced cellular permeability strongly suggests these compounds are up-and-coming antidiabetic agents. These findings demonstrate the efficacy of this integrated computational strategy, along with the utilization of rigorously filtered public databases, for accelerating the discovery of safer and more effective antidiabetic treatments.