Support vector classification for SAR of 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 use...
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| Published in | Journal of Shanghai University Vol. 10; no. 4; pp. 366 - 370 |
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| Main Author | |
| Format | Journal Article |
| Language | Chinese English |
| Published |
01.08.2006
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1007-6417 1863-236X |
| DOI | 10.1007/s11741-006-0016-7 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | 杨善升 陆文聪 纪晓波 陈念贻 |
| AuthorAffiliation | Department of Chemistry, College of Sciences, Shanghai University, Shanghai 200444, P.R. China |
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| Cites_doi | 10.1126/science.1718042 10.1002/cem.1180070402 10.1016/S0097-8485(01)00094-8 10.1021/ci00020a024 10.1177/002029400103400803 10.1038/339706a0 10.1016/B978-012213815-7/50008-X |
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| DocumentTitleAlternate | Support vector classification for SAR of 5-HT3 receptor antagonists |
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| References | D J Livingstone (16_CR5) 1996 D Domine (16_CR3) 1993; 7 B Bienfait (16_CR4) 1994; 34 A V Maricq (16_CR2) 1991; 254 A Derkach V.Suprenant (16_CR1) 1989; 339 M Trotter (16_CR8) 2001; 34 R Burbidge (16_CR7) 2001; 26 Nian-yi Chen (16_CR9) 2002; 19 Wen-cong Lu (16_CR10) 2002; 19 VN Vapnik (16_CR6) 1998 Hui Zhao (16_CR11) 2004; 62 |
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| Snippet | In this work, support vector classification （SVC） algorithm was used to build structure-activity relationship （SAR） model of the 5-hydroxytryptamine type 3... In this work, support vector classification (SVC) algorithm was used to build structure-activity relationship (SAR) model of the 5-hydroxytryptamine type 3... |
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| SubjectTerms | 5-HT3受体拮抗体 化学计量学 支持向量分类 结构-活性关系 |
| Title | Support vector classification for SAR of 5-HT3 receptor antagonists |
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