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

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, with D0012 and D0013 (2‐cyanopyrrolidine fingerprints G1, G5) offering a balance between stability and reactivity. In addition, the d4p_v1 tool ( https://github.com/Amincheminfom/d4p_v1 ) reliably distinguishes active and inactive DPP‐4i using molecular descriptors derived from input SMILES strings. Therefore, this study not only revealed the chemical space of DPP‐4i but also opened up a horizon in developing novel potent DPP‐4i for the management of type 2 diabetes mellitus (T2DM) in the future.