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A Computational Approach to Botanical Drug Design by Modeling Quantitative Composition–activity Relationship
Author(s) -
Wang Yi,
Wang Xuewei,
Cheng Yiyu
Publication year - 2006
Publication title -
chemical biology and drug design
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.59
H-Index - 77
eISSN - 1747-0285
pISSN - 1747-0277
DOI - 10.1111/j.1747-0285.2006.00431.x
Subject(s) - traditional medicine , composition (language) , quantitative structure–activity relationship , drug , linear regression , traditional chinese medicine , computer science , artificial intelligence , pharmacology , machine learning , medicine , alternative medicine , philosophy , linguistics , pathology
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.