YaTCM: Yet another Traditional Chinese Medicine Database for Drug Discovery

Traditional Chinese Medicine (TCM) has a long history of widespread clinical applications, especially in East Asia, and is becoming frequently used in Western countries. However, owing to extreme complicacy in both chemical ingredients and mechanism of action, a deep understanding of TCM is still difficult. To accelerate the modernization and popularization of TCM, a single comprehensive database is required, containing a wealth of TCM-related information and equipped with complete analytical tools. Here we present YaTCM (Yet another Traditional Chinese Medicine database), a free web-based toolkit, which provides comprehensive TCM information and is furnished with analysis tools. YaTCM allows a user to (1) identify the potential ingredients that are crucial to TCM herbs through similarity search and substructure search, (2) investigate the mechanism of action for TCM or prescription through pathway analysis and network pharmacology analysis, (3) predict potential targets for TCM molecules by multi-voting chemical similarity ensemble approach, and (4) explore functionally similar herb pairs. All these functions can lead to one systematic network for visualization of TCM recipes, herbs, ingredients, definite or putative protein targets, pathways, and diseases. This web service would help in uncovering the mechanism of action of TCM, revealing the essence of TCM theory and then promoting the drug discovery process. YaTCM is freely available at http://cadd.pharmacy.nankai.edu.cn/yatcm/home.

[1]  Jun Li,et al.  Anti-inflammatory and immunoregulatory effects of Yupingfeng (玉屏风) powder on chronic bronchitis rats , 2013, Chinese Journal of Integrative Medicine.

[2]  X. Su,et al.  Artemisinin: Discovery from the Chinese Herbal Garden , 2011, Cell.

[3]  R. Teschke,et al.  Review article: herbal hepatotoxicity – an update on traditional Chinese medicine preparations , 2014, Alimentary pharmacology & therapeutics.

[4]  Y. Mo,et al.  Cacalol, a natural sesquiterpene, induces apoptosis in breast cancer cells by modulating Akt-SREBP-FAS signaling pathway , 2011, Breast Cancer Research and Treatment.

[5]  Hao Ye,et al.  HIT: linking herbal active ingredients to targets , 2010, Nucleic Acids Res..

[6]  Jian Huang,et al.  CEMTDD: Chinese Ethnic Minority Traditional Drug Database , 2014, Apoptosis.

[7]  Z. Wang,et al.  PhIN: A Protein Pharmacology Interaction Network Database , 2015, CPT: pharmacometrics & systems pharmacology.

[8]  Zhao Fang,et al.  TCMID: traditional Chinese medicine integrative database for herb molecular mechanism analysis , 2012, Nucleic Acids Res..

[9]  J. Greef.  Perspective: All systems go , 2011, Nature.

[10]  John S. Delaney,et al.  Assessing the ability of chemical similarity measures to discriminate between active and inactive compounds , 1996, Molecular Diversity.

[11]  L. Ouyang,et al.  CEMTDD: The database for elucidating the relationships among herbs, compounds, targets and related diseases for Chinese ethnic minority traditional drugs , 2015, Oncotarget.

[12]  Thomas M. Ehrman,et al.  Phytochemical Databases of Chinese Herbal Constituents and Bioactive Plant Compounds with Known Target Specificities. , 2007 .

[13]  Calvin Yu-Chian Chen,et al.  TCM Database@Taiwan: The World's Largest Traditional Chinese Medicine Database for Drug Screening In Silico , 2011, PloS one.

[14]  Jun Li,et al.  Total extract of Yupingfeng attenuates bleomycin-induced pulmonary fibrosis in rats. , 2015, Phytomedicine : international journal of phytotherapy and phytopharmacology.

[15]  Jürgen Bajorath,et al.  Polypharmacology Directed Compound Data Mining: Identification of Promiscuous Chemotypes with Different Activity Profiles and Comparison to Approved Drugs , 2010, J. Chem. Inf. Model..

[16]  Ming-Wei Wang,et al.  Biological screening of natural products and drug innovation in China , 2007, Philosophical Transactions of the Royal Society B: Biological Sciences.

[17]  David Rogers,et al.  Extended-Connectivity Fingerprints , 2010, J. Chem. Inf. Model..

[18]  Michael J. Keiser,et al.  Relating protein pharmacology by ligand chemistry , 2007, Nature Biotechnology.

[19]  D. Kell,et al.  'Metabolite-likeness' as a criterion in the design and selection of pharmaceutical drug libraries. , 2009, Drug discovery today.

[20]  Tingjun Hou,et al.  A 3D Structure Database of Components from Chinese Traditional Medicinal Herbs , 2002, J. Chem. Inf. Comput. Sci..

[21]  Jian-Guo Jiang,et al.  Typical toxic components in traditional Chinese medicine , 2012, Expert opinion on drug safety.

[22]  Strohl,et al.  The role of natural products in a modern drug discovery program. , 2000, Drug discovery today.

[23]  Xiaojie Xu,et al.  CVDHD: a cardiovascular disease herbal database for drug discovery and network pharmacology , 2013, Journal of Cheminformatics.

[24]  Pedro J. Ballester,et al.  How Reliable Are Ligand-Centric Methods for Target Fishing? , 2015, bioRxiv.

[25]  Brian K. Shoichet,et al.  Chemical informatics uncovers a new role for moexipril as a novel inhibitor of cAMP phosphodiesterase-4 (PDE4) , 2013, Biochemical pharmacology.

[26]  S. Bent,et al.  Herbal Medicine in the United States: Review of Efficacy, Safety, and Regulation , 2008, Journal of General Internal Medicine.

[27]  Peter Ertl,et al.  JSME: a free molecule editor in JavaScript , 2013, Journal of Cheminformatics.

[28]  See-Tong Pang,et al.  Aristolochic acids and their derivatives are widely implicated in liver cancers in Taiwan and throughout Asia , 2017, Science Translational Medicine.

[29]  Minoru Kanehisa,et al.  KEGG: new perspectives on genomes, pathways, diseases and drugs , 2016, Nucleic Acids Res..

[30]  Yicheng He,et al.  Baicalin attenuates bleomycin-induced pulmonary fibrosis via adenosine A2a receptor related TGF-β1-induced ERK1/2 signaling pathway , 2016, BMC Pulmonary Medicine.

[31]  M. Soares,et al.  Antimalarial activity of physalins B, D, F, and G. , 2011, Journal of natural products.

[32]  Zheng Yin,et al.  Improving chemical similarity ensemble approach in target prediction , 2016, Journal of Cheminformatics.

[33]  Jacques Cohen,et al.  Bioinformatics—an introduction for computer scientists , 2004, CSUR.

[34]  Noboru Fukuda,et al.  Transforming growth factor-beta expression in cardiovascular organs in stroke-prone spontaneously hypertensive rats with the development of hypertension. , 2002, Hypertension research : official journal of the Japanese Society of Hypertension.

[35]  Xue Xu,et al.  Network pharmacology-based prediction of the active ingredients and potential targets of Chinese herbal Radix Curcumae formula for application to cardiovascular disease. , 2013, Journal of ethnopharmacology.

[36]  Wei Zhou,et al.  TCMSP: a database of systems pharmacology for drug discovery from herbal medicines , 2014, Journal of Cheminformatics.

[37]  Shan Miao,et al.  Cardioprotective Effect of Paeonol and Danshensu Combination on Isoproterenol-Induced Myocardial Injury in Rats , 2012, PloS one.

[38]  S. Peng,et al.  ERK in Learning and Memory: A Review of Recent Research , 2010, International journal of molecular sciences.

[39]  Jae Yong Ryu,et al.  A systems approach to traditional oriental medicine , 2015, Nature Biotechnology.

[40]  Zhiguo Xu.  Modernization: One step at a time , 2011, Nature.

[41]  Michael J. Keiser,et al.  Predicting new molecular targets for known drugs , 2009, Nature.

[42]  J. Lehár,et al.  Multi-target therapeutics: when the whole is greater than the sum of the parts. , 2007, Drug discovery today.

[43]  Brad T. Sherman,et al.  Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists , 2008, Nucleic acids research.

[44]  Tingjun Hou,et al.  Drug-likeness analysis of traditional Chinese medicines: prediction of drug-likeness using machine learning approaches. , 2012, Molecular pharmaceutics.

[45]  Ilya Raskin,et al.  Revisiting the ancient concept of botanical therapeutics. , 2007, Nature chemical biology.

[46]  Thorsten Meinl,et al.  KNIME: The Konstanz Information Miner , 2007, GfKl.

[47]  L. Peshkin,et al.  Exploiting polypharmacology for drug target deconvolution , 2014, Proceedings of the National Academy of Sciences.

[48]  Kevan M. Shokat,et al.  Chemical genetic discovery of targets and anti-targets for cancer polypharmacology , 2012, Nature.

[49]  Hsin-Hsi Chen,et al.  TCMGeneDIT: a database for associated traditional Chinese medicine, gene and disease information using text mining , 2008, BMC complementary and alternative medicine.

[50]  Mathias Dunkel,et al.  Super Natural II—a database of natural products , 2014, Nucleic Acids Res..

[51]  Y. Tu.  The discovery of artemisinin (qinghaosu) and gifts from Chinese medicine , 2011, Nature Medicine.

[52]  X. Chen,et al.  TTD: Therapeutic Target Database , 2002, Nucleic Acids Res..