Classification of mixtures of Chinese Herbal Medicines based on a self-organizing map (SOM)

Maolin Wang, Li Li, Changyuan Yu, Aixia Yan*, Zhongzhen ZHAO, Ge ZHANG, Miao Jiang, Aiping LYU, Johann Gasteiger

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

22 Citations (Scopus)


Chinese Herbal Medicines (CHMs) are typically mixtures of compounds and are often categorized into cold and hot according to the theory of Chinese Medicine. This classification is essential for guiding the clinical application of CHMs. In this study, three types of molecular descriptors were used to build models for classification of 59 CHMs with typical cold/hot properties in the training set taken from the original records on properties in China Pharmacopeia as reference. The accuracy and the Matthews correlation coefficient of the models were validated by a test set containing other 56 CHMs. The best model produced the accuracies of 94.92 % and 83.93 % on training set and test set, respectively. The MACCS fingerprint model is robust in predicting hot/cold properties of the CHMs from their major constituting compounds. This work shows how a classification model for data consisting of multi-components can be developed. The derived model can be used for the application of Chinese herbal medicines.

Original languageEnglish
Pages (from-to)109-115
Number of pages7
JournalMolecular Informatics
Issue number3-4
Publication statusPublished - 1 Apr 2016

Scopus Subject Areas

  • Structural Biology
  • Molecular Medicine
  • Drug Discovery
  • Computer Science Applications
  • Organic Chemistry

User-Defined Keywords

  • Chinese Herbal Medicines
  • Classification model
  • Classification of mixtures
  • cold and hot properties
  • self-organizing map


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