Naïve Bayes classification using 2D pharmacophore feature triplet vectors

Journal of Chemical Information and Modeling
Paul Watson

Abstract

A naïve Bayes classifier, employed in conjunction with 2D pharmacophore feature triplet vectors describing the molecules, is presented and validated. Molecules are described using a vector where each element in the vector contains the number of times a particular triplet of atom-based features separated by a set of topological distances occurs. Using the feature triplet vectors it is possible to generate naïve Bayes classifiers that predict whether molecules are likely to be active against a given target (or family of targets). Two retrospective validation experiments were performed using a range of actives from WOMBAT, the Prous Integrity database, and the Arena screening library. The performance of the classifiers was evaluated using enrichment curves, enrichment factors, and the BEDROC metric. The classifiers were found to give significant enrichments for the various test sets.

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Citations

Feb 16, 2012·Journal of Chemical Information and Modeling·Feixiong ChengYun Tang
Mar 23, 2013·Journal of Chemical Information and Modeling·Kathrin Heikamp, Jürgen Bajorath
Mar 20, 2014·Journal of the American Medical Informatics Association : JAMIA·Feixiong Cheng, Zhongming Zhao
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Jan 15, 2021·Toxicology in Vitro : an International Journal Published in Association with BIBRA·Xuan XuGuixia Liu
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Nov 7, 2014·Journal of Chemical Information and Modeling·Ling WangJun Xu
Oct 30, 2018·Chemical Research in Toxicology·Yue ChenWeihua Li
Apr 17, 2014·Journal of Chemical Information and Modeling·Xiao LiYun Tang
Apr 16, 2011·Journal of Chemical Information and Modeling·Feixiong ChengYun Tang
Nov 22, 2017·Journal of Chemical Information and Modeling·Xiao LiYong Zhao

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