In-silico studies in Chinese herbal medicines' research: evaluation of in-silico methodologies and phytochemical data sources, and a review of research to date.

The available databases that catalogue information on traditional Chinese medicines are reviewed in terms of their content and utility for in-silico research on Chinese herbal medicines, as too are the various protein database resources, and the software available for use in such studies. The software available for bioinformatics and 'omics studies of Chinese herbal medicines are summarised, and a critical evaluation given of the various in-silico methods applied in screening Chinese herbal medicines, including classification trees, neural networks, support vector machines, docking and inverse docking algorithms. Recommendations are made regarding any future in-silico studies of Chinese herbal medicines.

Phytochemical informatics and virtual screening of herbs used in Chinese medicine.

While many experimental and clinical studies of traditional Chinese medicine (TCM) have been reported over recent years, the applications of computational methods to drug discovery from Chinese herbs are still at an early stage. In the light of the spread of TCM to other parts of the world over the last few decades, and the growing number of publications in languages other than Chinese, this article focuses on work published in English and accessible to an international audience. Sources of information in appropriate format are particularly important for informatics, and the growing number of TCM-related databases is discussed.

In silico search for multi-target anti-inflammatories in Chinese herbs and formulas.

Chinese herbs were screened for compounds which may be active against four targets involved in inflammation, using pharmacophore-assisted docking. Multiple LigandScout (LS) pharmacophores built from ligand-receptor complexes in the protein databank (PDB) were first employed to select compounds. These compounds were then docked using LS-derived templates and ranked according to docking score.

Screening of herbal constituents for aromatase inhibitory activity.

Random Forest screening of the phytochemical constituents of 240 herbs used in traditional Chinese medicine identified a number of compounds as potential inhibitors of the human aromatase enzyme (CYP19). Molecular modelling/docking studies indicated that three of these compounds (myricetin, liquiritigenin and gossypetin) would be likely to form stable complexes with the enzyme. The results of the virtual screening studies were subsequently confirmed experimentally, by in vitro (fluorimetric) assay of the compounds' inhibitory activity.

Phytochemical informatics of traditional Chinese medicine and therapeutic relevance.

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.