Support vector classification for structure-activity-relationship of 1-（ 1H- 1,2,4-triazole- 1-yl）- 2-（ 2,4-difluorophenyl）-3-substituted-2- propanols  

作　　者：纪晓波 陆文聪 蔡煜东 陈念贻 

机构地区：[1]School of Materials Science and Engineering, Shanghai University, Shanghai 200072, P. R. China [2]Department of Chemistry, College of Sciences, Shanghai University, Shanghai 200444, P. R. China

出　　处：《Journal of Shanghai University(English Edition)》2007年第5期521-526,共6页上海大学学报（英文版）

基　　金：Project supported by the National Natural Science Foundation of China （Grant Nos.20373040, 20503015）

摘　　要：The support vector classification （SVC） was employed to make a model for classification of antifungal activities of 1-（1H-1,2,4-triazole-l-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.The support vector classification （SVC） was employed to make a model for classification of antifungal activities of 1-（1H-1,2,4-triazole-l-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.

关 键 词：triazole derivatives antifungal activity structure-activity relationship （SAR） support vector machine leave-one- out cross-validation （LOOCV） 

分 类 号：O623.42[理学—有机化学]