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December 4, 2025International Journal of Molecular SciencesOpen Access

Integrative Computational Approaches for the Discovery of Triazole-Based Urease Inhibitors: A Machine Learning, Virtual Screening, and Meta-Dynamics Framework

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Authors

CFCamila A. Flores-MoralesJFJavier Farías-AbarcaMOManuel I. Osorio

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Overview

This analysis employs a virtual screening approach to identify triazole-based urease inhibitors in Helicobacter pylori, suggesting a novel pathway for combatting antibiotic resistance.

Key Points

  • Triazole-based urease inhibitors were identified using pharmacophore modeling and machine learning.
  • The approach uncovered strong binding candidates, with stability confirmed by molecular dynamics simulations.
  • Ensemble docking followed by quantum-polarized ligand docking pinpointed novel ligands with significant binding energy.
  • Findings highlight the potential for combating antibiotic resistance by targeting urease in Helicobacter pylori.

Cite This Study

Flores-Morales et al. (2025) studied this question.

synapsesocial.com/papers/6930dc81ea1aef094cca2540https://doi.org/10.3390/ijms262311576
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Also Consider

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

  1. 1Rational Design of Novel Inhibitors: Integrating 3D‐QSAR and Molecular Dynamics2026 · 1 citations
  2. 2Drug Repurposing Uncovers New Chemical Scaffolds as Potent Urease Inhibitors: A Comprehensive Computational Study2026
  3. 3Computational study of amino acid-transition metal complexes as potential Helicobacter pylori urease enzyme inhibitors: DFT, molecular docking and ADMET analysis2026
  4. 4Novel indole-based sulfonate and sulfamate derivatives as potent and selective urease inhibitors targeting Helicobacter pylori: a promising therapeutic strategy2026
  5. 5Molecular Docking and Molecular Dynamics Simulations of the Urease Inhibitory Activity of 2,3‐Dihydroxybenzohydrazide Derivatives2024