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    Phytochemical Informatics of Traditional Chinese Medicine and Therapeutic Relevance
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    Pharmaceutical Sciences Division and Centre for Natural Medicines Research, King's College London, Franklin-Wilkins Building, 150 Stamford Street, London SE1 9NH, U.K.
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    Journal of Chemical Information and Modeling

    Cite this: J. Chem. Inf. Model. 2007, 47, 6, 2316–2334
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    https://doi.org/10.1021/ci700155t
    Published October 11, 2007
    Copyright © 2007 American Chemical Society
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    Abstract

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    Distribution patterns of 8411 compounds from 240 Chinese herbs were analyzed in relation to the herbal categories of traditional Chinese medicine (TCM), using Random Forest (RF) and self-organizing maps (SOM). RF was used first to construct TCM profiles of individual compounds, which describe their affinities for 28 major herbal categories, while simultaneously minimizing the level of noise associated with the complex array of diverse phytochemicals found in herbs from each category. Profiles were then reduced and visualized with SOM. The distribution of 10 major phytochemical classes, in relation to TCM profile, was delineated with SOM-Ward clustering. These classes comprised aliphatics, alkaloids, simple phenolics, lignans, quinones, polyphenols (flavonoids and tannins), and mono-, sesqui-, di-, and triterpenes (including sterols). Highly distinctive patterns of association between phytochemical class and TCM profile were revealed, suggesting that a strong phytochemical basis underlies the traditional language of Chinese medicine. Maps trained after random permutation of herbs assigned to each category were, by contrast, devoid of feature, providing additional evidence for the significance of these associations. Most classes were split into relatively few clusters, and further analysis revealed that simple descriptors, comprising skeletal type, molecular weight, and calculated log P, were in most cases able to readily discriminate within-class clusters. Relationships between TCM profile and predicted activities, relating to therapeutically important molecular targets, were explored and indicate that ethnopharmacological data could play an important role in pharmaceutical prospecting from Chinese herbs as well as identifying links between Chinese and Western medicine.

    Copyright © 2007 American Chemical Society

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    †

     Pharmaceutical Sciences Division.

    *

     Corresponding author e-mail:  [email protected].

    ‡

     Centre for Natural Medicines Research.

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    Maps for each phytochemical class trained using original data without prior construction of TCM profiles, and resulting from random permutation of herbal categories; a table containing information on predicted targets associated with each cluster shown in Figure 1, in addition to their possible therapeutic applications, and representative herbs; and details of the SOM-Ward clustering algorithm. This material is available free of charge via the Internet at http://pubs.acs.org.

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    Journal of Chemical Information and Modeling

    Cite this: J. Chem. Inf. Model. 2007, 47, 6, 2316–2334
    Click to copy citationCitation copied!
    https://doi.org/10.1021/ci700155t
    Published October 11, 2007
    Copyright © 2007 American Chemical Society
    Request reuse permissions

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