期刊
JOURNAL OF MOLECULAR GRAPHICS & MODELLING
卷 28, 期 8, 页码 883-890出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.jmgm.2010.03.008
关键词
Adenosine receptor; Antagonist; Classification; Laplacian-modified naive Bayesian; Recursive partitioning; Support vector machine
类别
资金
- Brain Korea 21 Project
- Ministry of Education, Science and Technology (MEST) [R15-2006-020]
- Korea Science and Engineering Foundation (KOSEF) through the Center for Cell Signaling & Drug Discovery Research at Ewha Womans University
Adenosine receptors (ARs) belong to the G-protein-coupled receptor (GPCR) superfamily and consist of four subtypes referred to as A(1), A(2A), A(2B), and A(3). It is important to develop potent and selective modulators of ARs for therapeutic applications. In order to develop reliable in silico models that can effectively classify antagonists of each AR, we carried out three machine learning methods: Laplacian-modified naive Bayesian, recursive partitioning, and support vector machine. The results for each classification model showed values high in accuracy, sensitivity, specificity, area under the receiver operating characteristic curve and Matthews correlation coefficient. By highlighting representative antagonists, the models demonstrated their power and usefulness, and these models could be utilized to predict potential AR antagonists in drug discovery. (C) 2010 Elsevier Inc. All rights reserved.
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