RESUMEN
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.
RESUMEN
In order to provide a new way and method for safety evaluation of Chinese materia medica (CMM) and also to provide a reference for conventional animal experiments, computer toxicity prediction technique and method were established to predict the cardiotoxicity of CMM. Mold2 software (version 2.0.0) was used to calculate molecular descriptors of 1034 chemical components. Then, the random forest (RF) method and the support vector machine (SVM) method were used to screen the descriptors. After that, boosting trees method, SVM, regularized discriminant analysis method and RF method were used to build up prediction model, respectively. Finally, the cardiotoxicity of chemical components was predicted by the quantitative structure-activity relationship (QSAR) model with the best accuracy and Kappa value. The results showed that by comparing the accuracy and Kappa value of prediction model, it was found that the RF model was the optimal algorithm model with 86.3%accuracy and the Kappa value of 0. 725. Through the prediction research on chemical components of Chinese herbs with toxicity recorded in the Pharmacopoeia of People’s Republic of China (version2010),suchasEvodia rutaecarpa,North bean root,Murraya incense,some meaningful results had been received. It was concluded that QSAR model on prediction research of chemical components of Chinese herbs provided important references for further experimental studies and clinical researches.