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September 24, 2025Archiv der PharmazieOpen Access

AI/ML‐Driven DPP‐4 Inhibitor Predictor (d4pᵥ1) for Enhanced Type 2 Diabetes Mellitus Management: Insights Into Chemical Space, Fingerprints, and Electrostatic Potential Maps

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Authors

AMAnu ManhasRDRitam DuttaSPStefano Piotto

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Overview

Fragment-based analysis identifies key fingerprints of DPP-4 inhibitors, suggesting efficient screening methods.

Key Points

  • The developed d4p_v1 tool accurately distinguishes active from inactive DPP-4 inhibitors for better type 2 diabetes management.
  • Key substructures linked to DPP-4 inhibition include 2-cyanopyrrolidine and other fingerprints, optimizing their design.
  • HOMO-LUMO gap analysis and electrostatic potential maps validate the key fingerprints identified in the study.
  • The study reveals chemical space insights for DPP-4 inhibitors, guiding the development of novel treatments.

Cite This Study

Manhas et al. (2025) studied this question.

synapsesocial.com/papers/68d6d8978b2b6861e4c3ebbbhttps://doi.org/10.1002/ardp.70106
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Machine learning framework for predicting DPP-4 inhibitory activity: a preliminary study on candidate screening and structural determinants2026
  2. 2Fragment-based QSAR study to explore the structural requirements of DPP-4 inhibitors: a stepping stone towards better type 2 diabetes mellitus management2024 · 2 citations
  3. 3Modeling Structure-Activity Relationships with Machine Learning to Identify DPP4 Inhibitors as potential Therapeutics for Type 2 Diabetes2026
  4. 4The Theoretical Substantiation of the Targeted Search for New DPP4 Inhibitors. Computational Studies of Potential Candidates2024
  5. 5IN SILICO DESIGN, 3D QSAR, PHARMACOPHORE MODELLING, AND MOLECULAR DOCKING STUDIES OF NOVEL ANTIDIABETIC AGENTS2026