ArticleBMC biology2025
Deep learning-based dipeptidyl peptidase IV inhibitor screening, experimental validation, and GaMD/LiGaMD analysis.
Article in BMC biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Revealing GRK5 Activation Features by Interpretable Machine Learning and Molecular Dynamics Simulation.International journal of molecular sciences · 2026Article
- Dual-Target Insight into Drug Discovery from Natural Products as Modulators of GLP-1 and the TXNIP-Thioredoxin Antioxidant System in Metabolic Syndrome.Antioxidants (Basel, Switzerland) · 2025Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundDipeptidyl peptidase-4 (DPP4) is considered a crucial enzyme in type 2 diabetes (T2D) treatment, targeted by inhibitors due to its role in cleaving glucagon-like peptide-1 (GLP-1). In this study, a novel DPP4 inhibitor screening strategy was developed, which significantly improved screening accuracy.
resultsIn this study, a DPP4 inhibitor screening method was developed, integrating receptor-based ConPLex, ligand-based KPGT, and molecular docking to enhance screening accuracy. Using this approach, four potential drugs were identified from the FDA database, achieving a 100% hit rate. Among these, Isavuconazonium demonstrated the highest inhibitory activity (IC
conclusionsOur study offers a robust approach and valuable insights for the development of DPP4 inhibitors, providing an effective means to investigate the binding and dissociation mechanisms between proteins and compounds.
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Registered trials
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