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Updated: Jun 17, 2025

Network Pharmacology Prediction and Metabolomics Validation of the Mechanism of Fructus Phyllanthi against Hyperlipidemia
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Building shape-focused pharmacophore models for effective docking screening.

Paola Moyano-Gómez1,2, Jukka V Lehtonen3,4, Olli T Pentikäinen1,2,5

  • 1MedChem.fi, Institute of Biomedicine, Integrative Physiology and Pharmacology, University of Turku, 20014, Turku, Finland.

Journal of Cheminformatics
|August 9, 2024
PubMed
Summary
This summary is machine-generated.

We developed O-LAP, a novel algorithm for molecular docking that improves accuracy by modeling protein binding cavities. This shape-focused pharmacophore approach enhances ligand identification in drug discovery.

Keywords:
BenchmarkingDistance-based graph clusteringDocking rescoringDrug discoveryFlexible-ligand molecular dockingShape similarityShape-focused pharmacophore modelingVirtual screening

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Area of Science:

  • Computational Chemistry
  • Structural Biology
  • Drug Discovery

Background:

  • Molecular docking performance can be enhanced by comparing ligand poses to inverted protein binding cavities.
  • Shape similarity and enrichment-driven optimization are key to improving docking accuracy.
  • Existing methods may not fully capture the complex shape and electrostatic properties of binding sites.

Purpose of the Study:

  • To introduce O-LAP, a novel shape-focused pharmacophore modeling algorithm.
  • To generate new cavity-filling models using graph clustering of overlapping atomic content.
  • To evaluate O-LAP's effectiveness in improving molecular docking enrichment and rescoring.

Main Methods:

  • Developed O-LAP, a C++/Qt5-based algorithm utilizing pairwise distance graph clustering.
  • Generated cavity-filling models using top-ranked poses of flexibly docked active ligands.
  • Benchmark-tested O-LAP with five drug targets, employing random training/test divisions and assessing enrichment.

Main Results:

  • O-LAP modeling significantly improved default docking enrichment in rescoring tasks.
  • Clustered models generated by O-LAP demonstrated effectiveness in rigid docking scenarios.
  • The algorithm successfully compared shape and electrostatic potentials against sampled molecular docking poses.

Conclusions:

  • O-LAP provides a novel approach to shape-focused pharmacophore modeling for drug discovery.
  • The algorithm effectively enhances molecular docking performance through improved binding site modeling.
  • O-LAP ensures high enrichment in both docking rescoring and rigid docking, validated by comprehensive testing.