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Updated: Jun 22, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Robust optimization of scoring functions for a target class.

Markus H J Seifert1

  • 1markus.seifert@4sc.com

Journal of Computer-Aided Molecular Design
|May 28, 2009
PubMed
Summary
This summary is machine-generated.

Optimizing scoring functions for protein-ligand docking can now target multiple protein kinases simultaneously. This novel approach enhances virtual screening accuracy and robustness, outperforming traditional methods.

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Last Updated: Jun 22, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

  • Computational chemistry
  • Structural biology
  • Drug discovery

Background:

  • Target-specific optimization of scoring functions improves virtual screening.
  • Current methods are often limited to single protein structures.
  • Protein kinases are a crucial drug target class.

Purpose of the Study:

  • To develop a method for simultaneous optimization of scoring functions across multiple protein kinase targets.
  • To enhance the robustness and applicability of scoring functions in virtual screening.
  • To improve the accuracy of identifying active molecules for kinase inhibitors.

Main Methods:

  • Utilized an ensemble of protein kinase structures.
  • Employed the Directory of Useful Decoys (DUD-E) ligand dataset.
  • Developed and applied a novel multi-factorial optimization procedure.
  • Reweighted different types of molecular interactions.

Main Results:

  • Scoring functions were successfully tuned for multiple kinase targets concurrently.
  • Virtual screening performance for kinases showed significant improvement.
  • The developed machine-learning strategy demonstrated greater robustness against database variations compared to QSAR models.
  • Reweighting of interaction types proved critical for enhanced screening performance.

Conclusions:

  • Simultaneous multi-target optimization of scoring functions is feasible and effective for protein kinase classes.
  • This approach offers improved robustness and performance in virtual screening.
  • The method provides a more versatile tool for drug discovery and development.