Integrative Computational Approaches for the Discovery of Triazole-Based Urease Inhibitors: A Machine Learning,...

Area of Science:

• Biochemistry
• Computational Chemistry
• Drug Discovery

Background:

• Helicobacter pylori urease (HpU) is crucial for bacterial survival and virulence.
• Antibiotic resistance necessitates novel therapeutic strategies targeting essential bacterial enzymes.

Purpose of the Study:

• To identify novel triazole-based inhibitors of HpU using a multistage computational pipeline.
• To validate the computational approach for discovering non-hydroxamic urease inhibitors.

Main Methods:

• Integrated pharmacophore modeling, machine learning, ensemble docking, and molecular dynamics simulations.
• Screened over seven million compounds, progressively filtering to identify promising candidates.
• Utilized quantum-polarized ligand docking and well-tempered metadynamics for binding energy and stability analysis.

Main Results:

• Identified seven promising HpU ligands with strong binding energies and stable metal coordination.
• Revealed three highly stable complexes (CA1, CA3, CA6) via molecular dynamics simulations.
• Free-energy landscapes indicated potent binding potential for CA3 and CA6, comparable to a reference inhibitor.

Conclusions:

• The computational strategy effectively identified potential non-hydroxamic HpU inhibitors.
• Selected triazole-based candidates show favorable ADMET properties for further in vitro evaluation.
• This study provides a rational basis for developing new urease inhibitors and expanding triazole scaffold discovery.