上海大学学报（英文版）
上海大學學報（英文版）
상해대학학보（영문판）
JOURNAL OF SHANGHAI UNIVERSITY (ENGLISH EDITION)
2006年
4期
366-370
,共5页
杨善升%陆文聪%纪晓波%陈念贻
楊善升%陸文聰%紀曉波%陳唸貽
양선승%륙문총%기효파%진념이
support vector classification%structure-activity relationship%chemometrics%5-HT3 receptor antagonists
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.
