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Related Experiment Video

Updated: Feb 14, 2026

Routine Collection of High-Resolution cryo-EM Datasets Using 200 KV Transmission Electron Microscope
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Decoys Selection in Benchmarking Datasets: Overview and Perspectives.

Manon Réau1, Florent Langenfeld1, Jean-François Zagury1

  • 1Laboratoire GBA, EA4627, Conservatoire National des Arts et Métiers, Paris, France.

Frontiers in Pharmacology
|February 9, 2018
PubMed
Summary
This summary is machine-generated.

Virtual Screening (VS) methods require careful selection of decoy compounds in benchmarking datasets for accurate performance evaluation. This review details the evolution of decoy selection and offers recommendations for designing unbiased datasets.

Keywords:
benchmarkingbenchmarking databasesdecoyligand-based drug designstructure-based drug designvirtual screening

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Area of Science:

  • Computational chemistry
  • Drug discovery
  • Bioinformatics

Background:

  • Virtual Screening (VS) identifies potential drug candidates by evaluating large compound libraries against biological targets.
  • Accurate performance evaluation of VS methods is crucial for reliable drug discovery outcomes.
  • Benchmarking datasets, comprising active compounds and decoys, are essential for assessing VS method efficacy.

Purpose of the Study:

  • To review the evolution of decoy selection strategies in Virtual Screening benchmarking datasets.
  • To highlight current trends in decoy selection aimed at minimizing bias.
  • To provide recommendations for the optimal selection and design of benchmarking datasets.

Main Methods:

  • Analysis of historical and current practices in decoy compound selection for VS benchmarking.
  • Examination of existing benchmarking databases and their decoy composition.
  • Literature review on methodologies for generating and validating negative control compounds.

Main Results:

  • Decoy selection has evolved from random choices to sophisticated, customized, or experimentally validated negative compounds.
  • Current benchmarking databases increasingly focus on minimizing biases in dataset composition.
  • The choice of decoys significantly impacts the perceived performance of VS methods.

Conclusions:

  • The selection of appropriate decoys is critical for unbiased evaluation of Virtual Screening performance.
  • Adopting advanced decoy selection strategies enhances the reliability of VS method assessment.
  • Recommendations are provided for constructing robust benchmarking datasets to advance drug discovery efforts.