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A classification study of human beta(3)-adrenergic receptor agonists using BCUT descriptors

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Indexed by:期刊论文

Date of Publication:2011-11-01

Journal:MOLECULAR DIVERSITY

Included Journals:PubMed、Scopus、SCIE

Volume:15

Issue:4

Page Number:877-887

ISSN No.:1381-1991

Key Words:Human beta(3)-adrenergic receptor agonists; Variable selection; Dragon descriptors; Random forest

Abstract:Experimental EC(50)s for 202 human beta(3)-AR agonists are used to develop classification models as a potential screening tool for a large library of target compounds before synthesis. A variable selection approach from random forests (VS-RF) is used to extract the structural information most relevant to the human beta(3)-AR activation properties of the collected data set. The obtained results indicate that the VS-RF method can be used for variable selection with smallest sets of non-redundant descriptors with highly predictive accuracy (Q(ex)%=96% for the external prediction set). Thus, the proposed VS-RF models should be helpful for screening of potential human beta(3)-AR agonists before chemical synthesis in drug development.

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