Rational Design of Novel Inhibitors: Integrating 3D‐QSAR and Molecular Dynamics

TL;DR

This paper introduces a computational strategy combining 3D-QSAR and molecular dynamics to design new triazole-based urease inhibitors for Helicobacter pylori.

Contribution

The study's novelty lies in integrating 3D-QSAR, CoMFA, CoMSIA, and MD simulations for rational drug design of urease inhibitors.

Findings

Pharmacophore models identified ZINC84668437, ZINC84669798, and ZINC244633273 as promising triazole-based urease inhibitors.

Molecular dynamics simulations confirmed the stability of ligand-urease complexes.

The integrated computational workflow provides a predictive framework for inhibitor design.

Abstract

This study presents an integrated computational framework that combines three‐dimensional quantitative structure–activity relationship (3D‐QSAR) modeling with molecular dynamics (MD) simulations to advance the rational design of triazole‐based urease inhibitors. Given the role of urease as a central virulence factor in Helicobacter pylori and its increasing relevance in antibiotic‐resistant infections, effective predictive methodologies are essential for early‐stage inhibitor development. A dataset of 54 triazole derivatives was examined using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA), generating statistically robust and predictive models that elucidate the steric, hydrophobic, and hydrogen‐bonding features underlying inhibitory potency. These insights, supported by molecular docking and structure–activity relationship analyses, informed the construction of a pharmacophore model used to screen the ZINC database. Several candidate molecules, including ZINC84668437, ZINC84669798, and ZINC244633273, emerged as computationally promising and demonstrated favorable predicted druglikeness and ADMET characteristics. MD simulations were subsequently employed to evaluate the dynamic stability and conformational behavior of the ligand–urease complexes, reinforcing the coherence of the integrated computational workflow. The primary contribution of this work resides in its methodological integration of complementary in silico approaches rather than in experimental validation. Although the findings offer mechanistic insights and prioritize potential lead compounds, they remain predictive and necessitate future empirical confirmation. Overall, the study establishes a rigorous and academically grounded computational strategy that can guide subsequent efforts in the design of selective urease inhibitors for H. pylori–associated diseases.

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