Type 2 Diabetes Mellitus (T2DM) is a chronic metabolic disorder characterized by insulin resistance and relative insulin deficiency. Dipeptidyl peptidase-4 (DPP-4) inhibitors have emerged as a significant therapeutic class for T2DM management. In this study, a comprehensive Computer-Aided Drug Design (CADD) approach was utilized to design novel DPP-4 inhibitors. A dataset of 21 benzoic-acid based xanthine derivatives with known DPP-4 inhibitory activity was used to generate predictive 3D QSAR and pharmacophore models using BIOVIA Discovery Studio 2021. The best 3D QSAR hypothesis exhibited a strong correlation coefficient of 0.772, highlighting the critical importance of hydrogen bond acceptors and hydrophobic features for receptor binding. This validated model was subsequently employed for the virtual screening of a diverse drug-like database, identifying IBS189112 as a top template hit. Following rigorous hit-to-lead optimization, a novel 1,3,5-triazine derivative was theoretically designed. Molecular docking simulations with the human DPP-4 enzyme (PDB ID: 5Y7H) revealed an excellent LibDock score of 129, with robust binding interactions at key catalytic residues including PHE357, TYR547, GLN553, ARG560, and LYS554. Furthermore, in silico ADMET and Lipinski rule evaluations confirmed its highly favorable pharmacokinetic profile. The purely computational findings suggest that the designed 1,3,5-triazine derivative represents a highly promising candidate for DPP-4 inhibition, warranting future wet-lab synthesis and biological evaluation for T2DM management.
Satyendra Singh Rathore* (Sun,) studied this question.