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

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.

Eleven prenylated flavonoids 1–11 were designed and synthesized according to SVM-based classification model for vasodilators. Their vasorelaxation activities were determined experimentally in rat aorta rings that were pretreated with phenylephrine (PE).Figure optionsDownload as PowerPoint slide