QSAR Study on Toxicity of Chemical Components of Chinese Materia Medica and Acute Toxicity of Rats / 中国中医药信息杂志
Chinese Journal of Information on Traditional Chinese Medicine
; (12): 43-46, 2016.
Article
en Zh
| WPRIM
| ID: wpr-483560
Biblioteca responsable:
WPRO
ABSTRACT
Objective To study computer toxicity prediction technology and predict the acute toxicity of Chinese materia medica; To provide a new way and method for safety evaluation of traditional Chinese medicine. Methods First, Mold2 software (version 2.0.0) was used to calculate molecular descriptors of 7409 chemical components. After preliminary screening of molecular descriptors, quantitative structure-activity relationship (QSAR) models were built up with Random Forest (RF) for screening the optimum prediction model. From the 83 kinds of toxic Chinese materia medica in Chinese Pharmacopoeia (2010 edition), acute toxicity of 60 kinds of Chinese materia medica reported from monomer structure (1692 chemical components) were under prediction.Results Totally 7409 pieces of data were obtained. When the descriptors were 52, RF modeling accuracy and Kappa were the highest, 0.712 and 0.436 respectively. Compound clusters were divided into 3 types according to optimum molecule descriptors (52). The accuracy and Kappa of the optimum model for the first type of compounds were 0.666 and 0.476 respectively; the accuracy and Kappa of the optimum model for the second type of compounds were 0.804 and 0.381 respectively; the accuracy and Kappa of the optimum model for the third type of compounds were 0.709 and 0.373 respectively. It was predicted that 60 kinds of Chinese materia medica containing 0 violent toxic compound, 2 high toxic compounds, 172 medium toxic compounds and 1518 low toxic compound.Conclusion QSAR model for prediction study on acute toxicity of chemical components of Chinese mareria medica can provide references combination medication and experimental studies.
Texto completo:
1
Base de datos:
WPRIM
Tipo de estudio:
Prognostic_studies
Idioma:
Zh
Revista:
Chinese Journal of Information on Traditional Chinese Medicine
Año:
2016
Tipo del documento:
Article