Multiscale In Silico Discovery of Next-Generation SGLT2 Inhibitors for Type 2 Diabetes: A Comparative Docking and Molecular Dynamics Study of Natural Metabolites and Synthetic Peptides

This study presents a two-step in silico strategy for identifying next-generation SGLT2 inhibitors for type 2 diabetes. First, 22 plant-derived metabolites were screened by molecular docking and cross-docking against SGLT2 structures 7VSI and 8HEZ. Among the phytochemicals, EGCG showed strong binding affinity, while the synthetic peptide EWTNF exhibited the highest docking affinity. Molecular dynamics simulations, including 10 ns production runs and three independent replicates, supported the stability of the best-performing complexes. MM/PBSA calculations further indicated favorable binding energetics, particularly for peptide-associated systems. Overall, the study highlights both natural metabolites and synthetic peptides as promising scaffolds for SGLT2 inhibition.