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Machine Learning Consensus Scoring Improves Performance Across Targets in Structure-Based Virtual Screening.

Spencer S Ericksen, Haozhen Wu, Huikun Zhang

  • 1Center for High Throughput Computing, Department of Computer Sciences, University of Wisconsin-Madison , 1210 W. Dayton St., Madison, Wisconsin 53706, United States.

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|June 28, 2017
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Summary
This summary is machine-generated.

Consensus scoring in virtual screening improves compound ranking accuracy. Novel methods like gradient boosting and mixture models offer further enhancements over traditional approaches for drug discovery.

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Area of Science:

  • Computational Chemistry and Cheminformatics
  • Drug Discovery and Development

Background:

  • Structure-based virtual screening is crucial for identifying potential drug candidates.
  • Ranking compounds using a single docking program can lead to variable and suboptimal performance.
  • Consensus scoring methods, combining multiple predictions, offer improved reliability.

Purpose of the Study:

  • To compare traditional consensus scoring with a novel unsupervised gradient boosting approach.
  • To develop and evaluate a statistical mixture model consensus score.
  • To assess the performance of these methods on benchmark virtual screening targets.

Main Methods:

  • Evaluated traditional consensus scoring (e.g., mean of quantile normalized scores).
  • Implemented and tested an unsupervised gradient boosting approach.
  • Developed a statistical mixture model combining score means and variances.
  • Utilized ROCAUC and EF1 metrics on 21 DUD-E benchmark targets.

Main Results:

  • Traditional consensus methods outperformed individual docking programs and showed robustness.
  • The mixture model and gradient boosting approaches provided further performance improvements.
  • These advanced methods demonstrated superior predictive performance compared to traditional consensus.

Conclusions:

  • Advanced consensus scoring methods, including gradient boosting and mixture models, enhance virtual screening accuracy.
  • These approaches are readily applicable to new targets in academic research.
  • They mitigate the risks associated with relying on single docking program predictions.