AI/ML-Driven DPP-4 Inhibitor Predictor (d4p_v1) for Enhanced Type 2 Diabetes Mellitus Management: Insights Into Chemical Space, Fingerprints, and Electrostatic Potential Maps.

ABSTRACT: Dipeptidyl peptidase-4 inhibitors (DPP-4i) represent a relatively new class of oral antidiabetic drugs. This study focuses on: (a) identifying favourable and unfavourable fingerprints governing DPP-4 inhibition using fragment-based analysis, (b) validating key fingerprints through HOMO-LUMO gap analysis and electrostatic potential (ESP) maps, and (c) developing AI/ML-driven DPP-4 predictor, an online cheminformatics tool for efficient DPP-4i screening using a trained, validated AI/ML model. The fragment-based QSAR model finds key substructures linked to potent DPP-4 inhibition, including 2-cyanopyrrolidine, 3-amino tetrahydropyran, and difluoro phenyl groups. D0010 (3-aminotetrahydropyran fingerprint G10) is the most reactive, while D0094 (difluorophenyl fingerprint G14) is the most stable

SUBMITTER: Manhas A 

PROVIDER: S-EPMC12455174 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature