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Support vector classification for structure-activity-relationship of 1-(1H-1,2,4-triazole-1-yl)-2-(2,4-difluorophenyl)-3-substituted-2-propanols.
- Source :
- Journal of Shanghai University; Oct2007, Vol. 11 Issue 5, p521-526, 6p
- Publication Year :
- 2007
-
Abstract
- The support vector classification (SVC) was employed to make a model for classification of antifungal activities of 1-(1H-1,2,4-triazole-1-yl)-2-(2,4-difluorophenyl)-3-substituted-2-propanols triazole derivatives. The compounds with high antifungal activities and those with low antifungal activities were compared on the basis of the following molecular descriptors: net atomic charge on the atom N connecting with R, dipole moment and heat of formation. By using the SVC, a mathematical model was constructed, which can predict the antifungal activities of the triazole derivatives, with an accuracy of 91% on the basis of the leave-one-out cross-validation (LOOCV) test. The results indicate that the performance of the SVC model can exceed that of the principal component analysis (PCA) and K-Nearest Neighbor (KNN) models for this real world data set. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10076417
- Volume :
- 11
- Issue :
- 5
- Database :
- Complementary Index
- Journal :
- Journal of Shanghai University
- Publication Type :
- Academic Journal
- Accession number :
- 49776182
- Full Text :
- https://doi.org/10.1007/s11741-007-0516-1