• natural product;
  • neural network;
  • quantitative composition–activity relationship;
  • rational drug design;
  • support vector regression

Herbal medicine has been successfully applied in clinical therapeutics throughout the world. Following the concept of quantitative composition–activity relationship, the presented study proposes a computational strategy to predict bioactivity of herbal medicine and design new botanical drug. As a case, the quantitative relationship between chemical composition and decreasing cholesterol effect of Qi-Xue-Bing-Zhi-Fang, a widely used herbal medicine in China, was investigated. Quantitative composition–activity relationship models generated by multiple linear regression, artificial neural networks, and support vector regression exhibited different capabilities of predictive accuracy. Moreover, the proportion of two active components of Qi-Xue-Bing-Zhi-Fang was optimized based on the quantitative composition–activity relationship model to obtain new formulation. Validation experiments showed that the optimized herbal medicine has greater activity. The results indicate that the presented method is an efficient approach to botanical drug design.