TY - JOUR
T1 - In silico classification of adenosine receptor antagonists using Laplacian-modified naïve Bayesian, support vector machine, and recursive partitioning
AU - Lee, Jin Hee
AU - Lee, Sunkyung
AU - Choi, Sun
PY - 2010/6
Y1 - 2010/6
N2 - Adenosine receptors (ARs) belong to the G-protein-coupled receptor (GPCR) superfamily and consist of four subtypes referred to as A1, A2A, A2B, and A3. 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 naïve 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.
AB - Adenosine receptors (ARs) belong to the G-protein-coupled receptor (GPCR) superfamily and consist of four subtypes referred to as A1, A2A, A2B, and A3. 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 naïve 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.
KW - Adenosine receptor
KW - Antagonist
KW - Classification
KW - Laplacian-modified naïve Bayesian
KW - Recursive partitioning
KW - Support vector machine
UR - http://www.scopus.com/inward/record.url?scp=77953547967&partnerID=8YFLogxK
U2 - 10.1016/j.jmgm.2010.03.008
DO - 10.1016/j.jmgm.2010.03.008
M3 - Article
C2 - 20447849
AN - SCOPUS:77953547967
SN - 1093-3263
VL - 28
SP - 883
EP - 890
JO - Journal of Molecular Graphics and Modelling
JF - Journal of Molecular Graphics and Modelling
IS - 8
ER -