期刊
BIOORGANIC & MEDICINAL CHEMISTRY
卷 16, 期 17, 页码 8151-8160出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.bmc.2008.07.031
关键词
support vector machine; classification model; vasorelaxation; prenylated flavonoids
资金
- School of Medicine Zhejiang University
- College of pharmaceutical sciences Zhejiang University
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.
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