Guohui Wei 1 , Xianjun Fu 1 , Zhenguo Wang 1 . Show Affiliations »
Abstract
BACKGROUND: The nature of Chinese herbal medicines (CHMs) is a bridge between traditional Chinese medicine and clinical application. Accurate nature identification of CHMs is essential for guiding the clinical application of CHMs. OBJECTIVE: To develop a new method for nature identification of CHMs according to compounds in CHMs. METHODS: The nature of a CHM is a comprehensive manifestation of the properties of various compounds in the CHM. In this study, 2012 CHM compounds were extracted to construct a compound data set. Molecular descriptors were utilized to build an identification model for classification of the cold-hot-neutral nature of CHM compounds. RESULTS: The predictive accuracy and confusion matrix were validated using the assembled data set. The best model produced accuracies of 96.5 ± 0.5% and 86.5 ± 1.5% on training set and test set, respectively. Furthermore, the identification model is robust in predicting the cold-hot-neutral nature of CHM compounds. CONCLUSION: This work shows how a classification model for medical nature identification can be developed. The derived model can be utilized for the application of CHMs. HIGHLIGHTS: To construct a nature identification model for analysis of the cold-hot-neutral nature of CHMs according to the compounds in CHMs. © AOAC INTERNATIONAL 2021. All rights reserved. For permissions, please email: journals.permissions@oup.com.
BACKGROUND: The nature of Chinese herbal medicines (CHMs) is a bridge between traditional Chinese medicine and clinical application. Accurate nature identification of CHMs is essential for guiding the clinical application of CHMs. OBJECTIVE: To develop a new method for nature identification of CHMs according to compounds in CHMs. METHODS: The nature of a CHM is a comprehensive manifestation of the properties of various compounds in the CHM. In this study, 2012 CHM compounds were extracted to construct a compound data set. Molecular descriptors were utilized to build an identification model for classification of the cold-hot-neutral nature of CHM compounds. RESULTS: The predictive accuracy and confusion matrix were validated using the assembled data set. The best model produced accuracies of 96.5 ± 0.5% and 86.5 ± 1.5% on training set and test set, respectively. Furthermore, the identification model is robust in predicting the cold-hot-neutral nature of CHM compounds. CONCLUSION: This work shows how a classification model for medical nature identification can be developed. The derived model can be utilized for the application of CHMs. HIGHLIGHTS: To construct a nature identification model for analysis of the cold-hot-neutral nature of CHMs according to the compounds in CHMs. © AOAC INTERNATIONAL 2021. All rights reserved. For permissions, please email: journals.permissions@oup.com.
Entities: Chemical
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Year: 2021
PMID: 33484262 DOI: 10.1093/jaoacint/qsab002
Source DB: PubMed Journal: J AOAC Int ISSN: 1060-3271 Impact factor: 1.913