A computational approach to botanical drug design by modeling quantitative composition-activity relationship.
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.
Wang
Yi
Y
Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Zheda Road 38#, Hangzhou 310027, China.
Wang
Xuewei
X
Cheng
Yiyu
Y
D000465:Algorithms; D000924:Anticholesteremic Agents / Q000737:chemistry* / Q000494:pharmacology; D015195:Drug Design; D004365:Drugs, Chinese Herbal / Q000737:chemistry* / Q000494:pharmacology; D029001:Herbal Medicine / Q000379:methods; D028321:Plant Preparations; D021281:Quantitative Structure-Activity Relationship
D000924:Anticholesteremic Agents; D004365:Drugs, Chinese Herbal; D028321:Plant Preparations