Literature DB >> 17918926

Designing compound subsets: comparison of random and rational approaches using statistical simulation.

Siew Kuen Yeap1, Rosalind J Walley, Mike Snarey, Willem P van Hoorn, Jonathan S Mason.   

Abstract

Compound subsets, which may be screened where it is not feasible or desirable to screen all available compounds, may be designed using rational or random selection. Literature on the relative performance of random versus rational selection reports conflicting observations, possibly because some random subsets might be more representative than others and perform better than subsets designed by rational means, or vice versa. In order to address this likelihood, we simulated a large number of rationally designed subsets for evaluation against an equally large number of randomly generated subsets. We found that our rationally designed subsets give higher mean hit rates compared to those of the random ones. We also compared subsets comprising random plates with subsets of random compounds and found that, while the mean hit rate of both is the same, the former demonstrates more variation in the hit rate. The choice of compound file, rational subset method, and ratio of the subset size to the compound file size are key factors in the relative performance of random and rational selection, and statistical simulation is a viable way to identify the selection approach appropriate for a subset.

Mesh:

Year:  2007        PMID: 17918926     DOI: 10.1021/ci600382m

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  5 in total

1.  Quantitative and qualitative models for carcinogenicity prediction for non-congeneric chemicals using CP ANN method for regulatory uses.

Authors:  Natalja Fjodorova; Marjan Vračko; Marjan Tušar; Aneta Jezierska; Marjana Novič; Ralph Kühne; Gerrit Schüürmann
Journal:  Mol Divers       Date:  2009-08-15       Impact factor: 2.943

2.  Plate-based diversity subset screening: an efficient paradigm for high throughput screening of a large screening file.

Authors:  Andrew S Bell; Joseph Bradley; Jeremy R Everett; Michelle Knight; Jens Loesel; John Mathias; David McLoughlin; James Mills; Robert E Sharp; Christine Williams; Terence P Wood
Journal:  Mol Divers       Date:  2013-04-05       Impact factor: 2.943

3.  Oxygen-containing fragments in natural products.

Authors:  Zoya Titarenko; Natalya Vasilevich; Vladimir Zernov; Michael Kirpichenok; Dmitry Genis
Journal:  J Comput Aided Mol Des       Date:  2012-12-28       Impact factor: 3.686

4.  Plate-based diversity subset screening generation 2: an improved paradigm for high-throughput screening of large compound files.

Authors:  Andrew S Bell; Joseph Bradley; Jeremy R Everett; Jens Loesel; David McLoughlin; James Mills; Marie-Claire Peakman; Robert E Sharp; Christine Williams; Hongyao Zhu
Journal:  Mol Divers       Date:  2016-09-08       Impact factor: 2.943

5.  Mining collections of compounds with Screening Assistant 2.

Authors:  Vincent Le Guilloux; Alban Arrault; Lionel Colliandre; Stéphane Bourg; Philippe Vayer; Luc Morin-Allory
Journal:  J Cheminform       Date:  2012-08-31       Impact factor: 5.514

  5 in total

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