Support vector classification for SAR of 5-HT3 receptor antagonists  

作　　者：杨善升 陆文聪 纪晓波 陈念贻 

机构地区：[1]Department of Chemistry, College of Sciences, Shanghai University, Shanghai 200444, P.R. China

出　　处：《Journal of Shanghai University(English Edition)》2006年第4期366-370,共5页上海大学学报（英文版）

基　　金：Project supported by National Natural Science Foundation of China（ Grant No. 20373040）

摘　　要：In this work, support vector classification （SVC） algorithm was used to build structure-activity relationship （SAR） model of the 5-hydroxytryptamine type 3 （5-HT3 ） receptor antagonists with 26 compounds. In a benchmark test, SVC was compared with several techniques of machine learning currently used in the field. The prediction performance of the model was discussed on the basis of the leave-one-out cross-validation. The results show that the accuracy of prediction of SVC model was higher than those of back propagation artificial neural network （BP ANN）, K-nearest neighbor （KNN） and Fisher methods.In this work, support vector classification （SVC） algorithm was used to build structure-activity relationship （SAR） model of the 5-hydroxytryptamine type 3 （5-HT3 ） receptor antagonists with 26 compounds. In a benchmark test, SVC was compared with several techniques of machine learning currently used in the field. The prediction performance of the model was discussed on the basis of the leave-one-out cross-validation. The results show that the accuracy of prediction of SVC model was higher than those of back propagation artificial neural network （BP ANN）, K-nearest neighbor （KNN） and Fisher methods.

关 键 词：support vector classification structure-activity relationship CHEMOMETRICS 5-HT3 receptor antagonists. 

分 类 号：O641[理学—物理化学]