首页|支持向量分类算法用于1-(1H-1,2,4-三唑-1-基)-2-(2,4-二氟苯基)-3-取代-2-丙醇化合物的构效关系研究

支持向量分类算法用于1-(1H-1,2,4-三唑-1-基)-2-(2,4-二氟苯基)-3-取代-2-丙醇化合物的构效关系研究

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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
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.

triazole derivatives, antifungal activity, structure-activity relationship (SAR), support vector machine, leave-one-out cross-validation (LOOCV).

纪晓波、陆文聪、蔡煜东、陈念贻

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School of Materials Science and Engineering, Shanghai University, Shanghai 200072, P. R. China

Department of Chemistry, College of Sciences, Shanghai University, Shanghai 200444, P. R. China

triazole derivatives, antifungal activity, structure-activity relationship (SAR), support vector machine, leave-one-out cross-validation (LOOCV).

国家自然科学基金国家自然科学基金

2037304020503015

2007

上海大学学报(英文版)
上海大学

上海大学学报(英文版)

影响因子：0.196
ISSN：1007-6417
年,卷(期)：2007.11(5)
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