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Related Concept Videos

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Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
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Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

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Towards an Enrichment Optimization Algorithm (EOA)-based Target Specific Docking Functions for Virtual Screening.

Jacob Spiegel1, Hanoch Senderowitz1

  • 1Department of Chemistry, Bar-Ilan University, Ramat-Gan, 5290002, Israel.

Molecular Informatics
|July 5, 2022
PubMed
Summary
This summary is machine-generated.

Developing target-specific scoring functions using the Enrichment Optimization Algorithm (EOA) can improve virtual screening (VS) performance in drug discovery. This method optimizes existing scoring functions for better hit identification and optimization.

Keywords:
EOAGOLDQSARdockingenrichment optimization algorithmtarget specific scoring functionsvirtual screening

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Area of Science:

  • Computational Chemistry
  • Cheminformatics
  • Drug Discovery

Background:

  • Docking-based virtual screening (VS) is crucial in drug discovery, offering insights into ligand binding modes and energies.
  • General docking programs may underperform on specific biological targets.
  • Developing target-specific scoring functions can enhance VS efficiency.

Purpose of the Study:

  • To present a method for creating target-specific scoring functions using the Enrichment Optimization Algorithm (EOA).
  • To adapt GOLD's ChemPLP scoring function for six specific targets using EOA.
  • To evaluate the performance improvement of these modified functions in VS.

Main Methods:

  • Utilized the Enrichment Optimization Algorithm (EOA) to derive Quantitative Structure-Activity Relationship (QSAR) models.
  • Retrieved active and inactive compounds for six targets from the DUD-E database.
  • Re-derived weights for GOLD's ChemPLP scoring function components to create target-specific functions.
  • Performed VS experiments, rescoring poses and re-docking compounds with modified functions.

Main Results:

  • Target-specific scoring functions, derived using EOA, improved virtual screening performance (AUC and EF1%) in many cases.
  • Both rescoring existing poses and re-docking with modified functions showed performance gains.
  • The effectiveness varied across different targets, indicating the need for further refinement.

Conclusions:

  • EOA-based optimization shows potential for developing effective target-specific scoring functions in VS.
  • Customized scoring functions can enhance hit identification and optimization in drug discovery pipelines.
  • Further research with diverse datasets and docking tools is warranted to broaden applicability.