A Novel Strategy of Structural Similarity Based Consensus Modeling. Issue 7 (1st July 2013)

• Record Type: Journal Article
• Authors: Lei, Beilei; Li, Jiazhong; Yao, Xiaojun
• Is Part Of: Molecular informatics. Volume 32:Issue 7(2013:Jul.)
• Page Start: 599
• Page End: 608
• Publication Date: 2013-07-01
• Subjects: Cheminformatics -- Periodicals; QSAR (Biochemistry) -- Periodicals; Structure-activity relationships (Biochemistry) -- Periodicals; Drugs -- Structure-activity relationships -- Periodicals; 615.19
• DOI: 10.1002/minf.201200170
• Languages: English
• ISSNs: 1868-1743

Abstract

<abstract abstract-type="main" xml:lang="en"> <title>Abstract</title> <p>A novel strategy of "structural similarity based consensus modeling" (SSCM) based on "model distance and <boxed-text content-type="graphic" position="anchor" orientation="portrait"><graphic position="anchor" mimetype="image" xlink:href="ark:/27927/pgg271mcfxr" orientation="portrait" xlink:type="simple" xmlns:xlink="http://www.w3.org/1999/xlink" /></boxed-text> guided model selection" (MD‐QGMS) submodel set was proposed. The SSCM strategy is built upon a hypothesis, that is, similar compounds are most probably predicted more accurately by a same submodel among a model population, which can be concluded from the fact that models employing a different set of descriptors can predict compounds with specific structures more accurately. It is proved that the proposed SSCM strategy can remarkably improve the external prediction ability of QSAR models by employing two different datasets. In future, the proposed SSCM strategy may provide a new direction to develop more accurate predictive models.</p> </abstract>