Identification of SVM-based classification model, synthesis and evaluation of prenylated flavonoids as vasorelaxant agents

被引：25

作者：

Dong, Xiaowu ^{[1
]}

Liu, Yujie ^{[1
]}

Yan, Jingying ^{[1
]}

Jiang, Chaoyi ^{[1
]}

Chen, Jing ^{[1
]}

Liu, Tao ^{[1
]}

Hu, Yongzhou ^{[1
]}

机构：

[1] Zhejiang Univ, ZJU ENS Joint Lab Med Chem, Coll Pharmaceut Sci, Hangzhou 310058, Zhejiang, Peoples R China

来源：

BIOORGANIC & MEDICINAL CHEMISTRY | 2008年 / 16卷 / 17期

关键词：

support vector machine; classification model; vasorelaxation; prenylated flavonoids;

D O I：

10.1016/j.bmc.2008.07.031

中图分类号：

Q5 [生物化学]; Q7 [分子生物学];

学科分类号：

071010 ; 081704 ;

摘要：

Support vector machine (SVM) was applied to predict vasorelaxation effect of different structural molecules. A good classification model had been established, and the accuracy in prediction for the training, test, and overall datasets was 93.0%, 82.6%, and 89.5%, respectively. Furthermore, the model was used to predict the activity of a series of prenylated flavonoids. According to the estimated result, eleven molecules 1-11 were selected and synthesized. Their vasodilatory activities were determined experimentally in rat aorta rings that were pretreated with phenylephrine ( PE). Structure-activity relationship (SAR) analysis revealed that flavanone derivatives showed the most potent activities, while flavone and chalcone derivatives exhibited medium activities. (C) 2008 Elsevier Ltd. All rights reserved.

引用

页码：8151 / 8160

页数：10

共 27 条

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