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Support vector machine for SAR/QSAR of phenethyl-amines

Acta Pharmacologica Sinica 2007年第7期28卷 1075-1086页

作者： Bing NIU~2,Wen-cong LU~(2,3,6),Shan-sheng YANG~2,Yu-dong CAI~(3,4,6),Guo-zheng LI~5 ~2 College of Material Science and Engineering,shanghai University,shanghai 200444,china ~3 Laboratory of Chemical Data Mining,department of Chemistry,College of sciences,shanghai University,shanghai 200444,china ~4 department of combinatorics and geometry,cas-mpg partner institute for computational biology,shanghai institutes for biological sciences,chinese academy of sciences,shanghai20003.,china ~5 School of Computer Science and Engineering,shanghai University,shanghai 200072,china College of Material Science and Engineering Shanghai University Shanghai China department of Chemistry Laboratory of Chemical Data Mining College of Sciences Shanghai University Shanghai China department of combinatorics and geometry CAS-MPG Partner Institute for Computational Biology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Shanghai China School of Computer Science and Engineering Shanghai Univeristy Shanghai China

Aim:To discriminate 32 phenethyl-amines between antagonists and agonists,and predict the activities of these ***:The support vectormachine (SVM) is employed to investigate the structure-activity relationship(SAR)/quantitative structure-activity relationship (QSAR) of phenethyl-aminesbased on molecular ***:By using the leave-one-out cross-vali-dation (LOOCV) test,1 optimal SAR and 2 optimal QSAR models for agonists andantagonists were *** accuracy of prediction for the classification ofphenethyl-amines by using the LOOCV test is 91.67%,and the accuracy of predic-tion for the classification of phenethyl-amines by using the independent test is100%;the results are better than those of the Fisher,the artificial neural network(ANN),and the K-nearest neighbor models for this real world *** RMSE(root mean square error)of antagonists' QSAR model is 0.5881,and the RMSE ofagonists' QSAR model is 0.4779,which are better than those of the multiple linearregression,partial least squares,and ANN models for this real world ***:The SVM can be used to investigate the SAR and QSAR of phenethyl-amines and could be a promising tool in the field of SAR/QSAR research.

关键词： support vector machine QSAR phenethylamines antagonists agonists

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