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

Bioorg Med Chem. 2008 Sep 1;16(17):8151-60. doi: 10.1016/j.bmc.2008.07.031. Epub 2008 Jul 20.

Abstract

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Animals
  • Aorta, Thoracic / drug effects*
  • Aorta, Thoracic / physiology
  • Computer Simulation*
  • Databases, Factual
  • Dose-Response Relationship, Drug
  • Flavonoids / chemical synthesis
  • Flavonoids / chemistry
  • Flavonoids / pharmacology*
  • Male
  • Models, Chemical*
  • Molecular Structure
  • Organ Culture Techniques
  • Phenylephrine / pharmacology
  • Predictive Value of Tests
  • Rats
  • Rats, Sprague-Dawley
  • Stereoisomerism
  • Structure-Activity Relationship
  • Vasoconstrictor Agents / pharmacology
  • Vasodilator Agents / chemical synthesis
  • Vasodilator Agents / chemistry
  • Vasodilator Agents / pharmacology*

Substances

  • Flavonoids
  • Vasoconstrictor Agents
  • Vasodilator Agents
  • Phenylephrine