TCM Database@Taiwan: The World's Largest Traditional Chinese Medicine Database for Drug Screening In Silico
Name
Chen-2011-TCM Database@Taiwan.pdf
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517.7 KB
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Adobe PDF
Checksum (MD5)
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Author(s)
Chen, Yu-Chian
Date Issued
January 2011
Journal
PLoS ONE
Publisher
Public Library of Science
Citation
Chen, Calvin Yu-Chian. "TCM Database@Taiwan: The World's Largest Traditional Chinese Medicine Database for Drug Screening In Silico." PLoS ONE 6(1): e15939.
Version
Final published version
Abstract
Rapid advancing computational technologies have greatly speeded up the development of computer-aided drug design (CADD). Recently, pharmaceutical companies have increasingly shifted their attentions toward traditional Chinese medicine (TCM) for novel lead compounds. Despite the growing number of studies on TCM, there is no free 3D small molecular structure database of TCM available for virtual screening or molecular simulation. To address this shortcoming, we have constructed TCM Database@Taiwan (http://tcm.cmu.edu.tw/) based on information collected from Chinese medical texts and scientific publications. TCM Database@Taiwan is currently the world's largest non-commercial TCM database. This web-based database contains more than 20,000 pure compounds isolated from 453 TCM ingredients. Both cdx (2D) and Tripos mol2 (3D) formats of each pure compound in the database are available for download and virtual screening. The TCM database includes both simple and advanced web-based query options that can specify search clauses, such as molecular properties, substructures, TCM ingredients, and TCM classification, based on intended drug actions. The TCM database can be easily accessed by all researchers conducting CADD. Over the last eight years, numerous volunteers have devoted their time to analyze TCM ingredients from Chinese medical texts as well as to construct structure files for each isolated compound. We believe that TCM Database@Taiwan will be a milestone on the path towards modernizing traditional Chinese medicine.
MIT Department
Massachusetts Institute of Technology. Computational and Systems Biology Program
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1371/journal.pone.0015939
