Herbal Ingredients In-Vivo Metabolism


The HIM database manually collected almost all the available in vivo metabolism information for herbal active ingredients, as well as their corresponding bioactivity, organs and/or tissues distribution, toxicity, ADME and the clinical research profile when possible. The paper is: Hong Kang, Kailin Tang, Qi Liu, Yi Sun, Qi Huang, Ruixin Zhu, Jun Gao, Duanfeng Zhang, Chenggang Huang and Zhiwei Cao: HIM-herbal ingredients in-vivo metabolism database. Journal of Cheminformatics 2013, 5(1):28. We are grateful to the authors for creating and curating this resource, and for allowing us to incorporate its structures in ZINC.

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Chemical Diversity and Clustering

We assess the chemical diversity of a subset by clustering the molecules. First, we sort ligands by increasing molecular weight. Then, we use the SUBSET 1.0 algorithm ( Voigt JH, Bienfait B, Wang S, Nicklaus MC. JCICS, 2001, 41, 702-12) to progressively select compounds that differ from those previously selected by at least the Tanimoto cutoff, using ChemAxon default fingerprints. The resulting representatives have two interesting properties:

  • 1) Each representative differs from all the others by at least the Tanitmoto cutoff and
  • 2) All the molecules in the subset are within the Tanimoto cutoff of at least one representative.
Thus the representatives can be said to "cover" the chemical space of the subset at a given Tanimoto level. N/A indicates that clustering is pending.

Tanimoto Cutoff Level 60% 70% 80% 90% 100%
Number of Representatives 80 110 133 184 663

Physical Property Distributions

We compute the physical properties of each molecule in the subset, and graph them below.   Download Calculated Physical Properties

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