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Optimization of CAMD techniques 3. Virtual screening enrichment studies: a help or hindrance in tool selection?

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Abstract

Over the last few years many articles have been published in an attempt to provide performance benchmarks for virtual screening tools. While this research has imparted useful insights, the myriad variables controlling said studies place significant limits on results interpretability. Here we investigate the effects of these variables, including analysis of calculation setup variation, the effect of target choice, active/decoy set selection (with particular emphasis on the effect of analogue bias) and enrichment data interpretation. In addition the optimization of the publicly available DUD benchmark sets through analogue bias removal is discussed, as is their augmentation through the addition of large diverse data sets collated using WOMBAT.

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Acknowledgments

This work was supported in part by support the New Mexico Tobacco Settlement fund (TIO). Thanks go to Andrei Leitão of UNM Biocomputing for his help in differentiating WOMBAT HIV NNRTI/NRTI compounds.

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Authors and Affiliations

  1. 5 Research Parkway, Wallingford, CT, 06492, USA

    Andrew C. Good

  2. Division of Biocomputing, Department of Biochemistry and Molecular Biology, MSC11 6145, University of New Mexico School of Medicine, Albuquerque, NM, 87131, USA

    Tudor I. Oprea

Authors
  1. Andrew C. Good
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  2. Tudor I. Oprea
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Correspondence to Andrew C. Good.

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Good, A.C., Oprea, T.I. Optimization of CAMD techniques 3. Virtual screening enrichment studies: a help or hindrance in tool selection?. J Comput Aided Mol Des 22, 169–178 (2008). https://doi.org/10.1007/s10822-007-9167-2

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  • DOI: https://doi.org/10.1007/s10822-007-9167-2

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