Support vector classification for SAR of 5-HT3 receptor antagonists

• Published in: Journal of Shanghai University Vol. 10; no. 4; pp. 366 - 370
• Main Author: 杨善升 陆文聪 纪晓波 陈念贻
• Format: Journal Article
• Language: Chinese; English
• Published: 01.08.2006
• Subjects: 5-HT3受体拮抗体; 化学计量学; 支持向量分类; 结构-活性关系
• ISSN: 1007-6417; 1863-236X
• DOI: 10.1007/s11741-006-0016-7

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.