Support vector machine for SAR/QSAR of phenethyl-amines  

作　　者：Bing NIU Wen-cong LU Shan-sheng YANG Yu-dong CAI Guo-zheng LI 

机构地区：[1]College of Material Science and Engineering, Shanghai University, Shanghai 200444, China [2]Laboratory of Chemical Data Mining, Department of Chemistry, College of Sciences, Shanghai University, Shanghai 200444, China [3]Department of Combinatorics and Geometry, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China [4]School of Computer Science and Engineering, Shanghai University, Shanghai 200072, China

出　　处：《Acta Pharmacologica Sinica》2007年第7期1075-1086,共12页中国药理学报（英文版）

基　　金：Project supported by the National Natural Science Foundation of China(№ 20373040 and 20503015)

摘　　要：Aim： To discriminate 32 phenethyl-amines between antagonists and agonists, and predict the activities of these compounds. Methods： The support vector machine （SVM） is employed to investigate the structure-activity relationship （SAR）/quantitative structure-activity relationship （QSAR） of phenethyl-amines based on molecular descriptors. Results： By using the leave-one-out cross-validation （LOOCV） test, 1 optimal SAR and 2 optimal QSAR models for agonists and antagonists were attained. The accuracy of prediction for the classification of phenethyl-amines by using the LOOCV test is 91.67%, and the accuracy of prediction for the classification of phenethyl-amines by using the independent test is 100%; the results are better than those of the Fisher, the artificial neural network （ANN）, and the K-nearest neighbor models for this real world data. The RMSE （root mean square error）of antagonists＇ QSAR model is 0.5881, and the RMSE of agonists＇ QSAR model is 0.4779, which are better than those of the multiple linear regression, partial least squares, and ANN models for this real world data. Conclusion： 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.Aim： To discriminate 32 phenethyl-amines between antagonists and agonists, and predict the activities of these compounds. Methods： The support vector machine （SVM） is employed to investigate the structure-activity relationship （SAR）/quantitative structure-activity relationship （QSAR） of phenethyl-amines based on molecular descriptors. Results： By using the leave-one-out cross-validation （LOOCV） test, 1 optimal SAR and 2 optimal QSAR models for agonists and antagonists were attained. The accuracy of prediction for the classification of phenethyl-amines by using the LOOCV test is 91.67%, and the accuracy of prediction for the classification of phenethyl-amines by using the independent test is 100%; the results are better than those of the Fisher, the artificial neural network （ANN）, and the K-nearest neighbor models for this real world data. The RMSE （root mean square error）of antagonists＇ QSAR model is 0.5881, and the RMSE of agonists＇ QSAR model is 0.4779, which are better than those of the multiple linear regression, partial least squares, and ANN models for this real world data. Conclusion： 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 phenethyl amines ANTAGONISTS AGONISTS 

分 类 号：R971[医药卫生—药品]