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Support vector classification for SAR of 5-HT3 receptor antagonists.
- Source :
- Journal of Shanghai University; Aug2006, Vol. 10 Issue 4, p366-370, 5p
- Publication Year :
- 2006
-
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-HT<subscript>3</subscript>) 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10076417
- Volume :
- 10
- Issue :
- 4
- Database :
- Complementary Index
- Journal :
- Journal of Shanghai University
- Publication Type :
- Academic Journal
- Accession number :
- 49671813
- Full Text :
- https://doi.org/10.1007/s11741-006-0016-7