Machine Learning–Driven Discovery of Dietary Polyphenol DPP-4 Inhibitors via Molecular Docking
TL;DR
This study aimed to identify polyphenolic compounds derived from dietary sources as potential DPP-4 inhibitors through an in silico pipeline integrating machine learning (ML) and molecular docking.
Abstract
T2DM is a chronic metabolic disorder of rising global prevalence, in which DPP-4 serves as a key therapeutic target through its role in incretin hormone degradation. This study aimed to identify polyphenolic compounds derived from dietary sources as potential DPP-4 inhibitors through an in silico pipeline integrating machine learning (ML) and molecular docking. The bioactivity dataset for DPP-4 was retrieved from ChEMBL (CHEMBL284), processed into a binary classification dataset, and represented using 2048-bit Morgan fingerprints combined with five RDKit descriptors. Six ML models were integrated into a soft-voting ensemble, achieving a Matthews correlation coefficient (MCC) of 0.8334 and an AUC-ROC of 0.9739. Screening of 162 compounds from the Phenol-Explorer database yielded 11 potential active inhibitors. Molecular docking identified hesperetin (−8.548 kcal/mol) as the leading candidate, followed by pelargonidin, daidzein, and naringenin, with key binding residues including Ser209, Glu205/206, Tyr631, Arg125, and Tyr662.