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Related Experiment Video

Updated: Mar 31, 2026

Nano-Differential Scanning Fluorimetry for Screening in Fragment-based Lead Discovery
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The statistics of virtual screening and lead optimization.

Mark McGann, Anthony Nicholls, Istvan Enyedy

    Journal of Computer-Aided Molecular Design
    |October 21, 2015
    PubMed
    Summary

    Analytic formulae provide a simple way to estimate errors for virtual screening metrics like enrichment factor and area under the ROC curve. These estimates closely match bootstrapping results, offering a faster alternative.

    Area of Science:

    • Computational chemistry
    • Drug discovery
    • Bioinformatics

    Background:

    • Virtual screening is crucial for identifying potential drug candidates.
    • Enrichment factor (EF) and area under the ROC curve (AUC) are common metrics for evaluating virtual screening performance.
    • Estimating the reliability and error of these metrics is essential for robust analysis.

    Purpose of the Study:

    • To introduce and validate analytic formulae for estimating errors in virtual screening metrics.
    • To compare the accuracy of analytic error estimates with bootstrapping methods.
    • To highlight the computational advantages of analytic formulae.

    Main Methods:

    • Development of analytic formulae to calculate the variance of enrichment factor and AUC.
    • Comparison of analytic error estimates against bootstrapping error estimates.

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  • Discussion of methods to convert variance into standard error bars.

    • Main Results:

    • Analytic error estimates show excellent agreement with bootstrapping for AUC.
    • Analytic error estimates demonstrate good agreement with bootstrapping for EF.
    • Analytic formulae are computationally inexpensive, requiring only metric values and counts of actives/inactives.

    Conclusions:

    • Analytic formulae offer a computationally efficient and accurate method for estimating errors in virtual screening metrics.
    • These formulae provide a valuable alternative to computationally intensive bootstrapping methods.
    • The simplicity and data requirements of analytic formulae facilitate their widespread adoption in virtual screening studies.