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Related Experiment Video

Updated: Jun 9, 2026

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Comparative Data Analysis of Virtual Screening Methodologies for Predicting Urease Inhibitory Activity.

Elizabeth Valdés-Muñoz1,2, Gabriel J Olguín-Orellana3, Sofía E Ríos-Rozas2

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Summary
This summary is machine-generated.

This study optimizes structure-based virtual screening (SBVS) for drug discovery by comparing docking and data fusion methods. Ensemble docking (ED) and molecular mechanics/generalized Born surface area (MM-GBSA) showed best compound ranking, with minimum data fusion proving most robust.

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Area of Science:

  • Computational chemistry and cheminformatics
  • Drug discovery and development
  • Molecular modeling and simulation

Background:

  • Structure-based virtual screening (SBVS) is crucial for identifying drug candidates.
  • SBVS accuracy depends heavily on chosen methodologies, scoring functions, and data processing.
  • Optimizing SBVS protocols is essential for efficient drug discovery pipelines.

Purpose of the Study:

  • To systematically evaluate and compare different SBVS protocol variants for drug discovery.
  • To assess the impact of data fusion techniques and pose number on ligand ranking accuracy.
  • To refine SBVS workflows for enhanced ligand prioritization against therapeutic targets.

Main Methods:

  • Employed five SBVS variants: molecular docking, induced-fit docking (IFD), quantum-polarized ligand docking (QPLD), ensemble docking (ED), and MM-GBSA.
  • Utilized four crystallographic structures of *Helicobacter pylori* urease from the PDB.
  • Assessed performance using correlation metrics (Spearman, Pearson) and error measures (MAE, RMSE, IRM), alongside six data fusion techniques.

Main Results:

  • Ensemble docking (ED) and MM-GBSA demonstrated superior performance in compound ranking.
  • MM-GBSA showed higher errors in absolute binding energy predictions compared to ED.
  • The minimum data fusion approach proved robust across varying numbers of docking poses.
  • pIC50 values yielded higher Pearson correlations for affinity prediction than IC50 values.

Conclusions:

  • MM-GBSA and ED are effective methods for compound ranking in SBVS.
  • Minimum data fusion is a reliable strategy for aggregating docking results.
  • pIC50 is a more suitable metric for affinity prediction in correlation analyses.
  • Optimized SBVS workflows enhance ligand prioritization for drug discovery campaigns.