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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. 相似文献
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1 Introduction The 5-hydroxytryptamine type 3 (5-HT3) receptorantagonists[1-2]are currently used in the treatment ofchemotherapy and radiotherapy induced emesis . Thecompounds are based onthe parent structure showninFig.1 ,the aromatic systems include mono- and bicy-clic rings ,with or without heteroatoms ,and with vari-ous substitution patterns .This range of structural vari-ation makes it difficult to treat the analysis of thesecompounds .Fig .1 Parent structure of 5-HT3antagonists With t… 相似文献
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