关键词: 支持向量学习机, 埃坡霉素, 分子描述符, K阶形状参数

Abstract: In order to predict the antitumor activities of various epothilone analogues, a set of molecular descriptors, including electronic, topological, and geometric descriptors, and molecular shape indices (K-order moment shape indices), were calculated to characterize the structural and physicochemical properties for 150 compounds. The 30 descriptors selected with genetic algorithm were employed to establish the classification and prediction model of epothilone analogues by using support vector machine (SVM). This SVM system gives a total prediction accuracy of 83.3% by the leave-one-out method and that of 80.6% by the 5-fold cross-validation method. The present study indicates that K-order moment shape indices are useful for description of configuration isomers, and SVM is a facilitating tool in prediction of antitumor activity of epothilone analogues.

Key words: Support vector machine, Epothilone, Molecular descriptors, K-order moment shape index