A classification study of human beta(3)-adrenergic receptor agonists using BCUT descriptors
Release Time:2019-03-09 Hits:
Indexed by: Journal Article
Date of Publication: 2011-11-01
Journal: MOLECULAR DIVERSITY
Included Journals: SCIE、Scopus、PubMed
Volume: 15
Issue: 4
Page Number: 877-887
ISSN: 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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