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10.1038/srep25462 http://scihub22266oqcxt.onion/10.1038/srep25462 C4857115!4857115 !27145869     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=27145869 &cmd=llinks): Failed to open stream: HTTP request failed! 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In silico identification of anti-cancer compounds and plants from traditional Chinese medicine database |pdf=|usr=}}{{27145869 }} gab.com Text In silico identification of anti-cancer compounds and plants from traditional Chinese medicine database JO: Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=27145869 #FOAvip There is a constant demand to develop new, effective, and affordable anti-cancer drugs. The traditional Chinese medicine (TCM) is a valuable and alternative resource for identifying novel anti-cancer agents. In this study, we aim to identify the anti-cancer compounds and plants from the TCM database by using cheminformatics. We first predicted 5278 anti-cancer compounds from TCM database. The top 346 compounds were highly potent active in the 60 cell lines test. Similarity analysis revealed that 75% of the 5278 compounds are highly similar to the approved anti-cancer drugs. Based on the predicted anti-cancer compounds, we identified 57 anti-cancer plants by activity enrichment. The identified plants are widely distributed in 46 genera and 28 families, which broadens the scope of the anti-cancer drug screening. Finally, we constructed a network of predicted anti-cancer plants and approved drugs based on the above results. The network highlighted the supportive role of the predicted plant in the development of anti-cancer drug and suggested different molecular anti-cancer mechanisms of the plants. Our study suggests that the predicted compounds and plants from TCM database offer an attractive starting point and a broader scope to mine for potential anti-cancer agents. Twit Text FOAVip In silico identification of anti-cancer compounds and plants from traditional Chinese medicine database ????Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=27145869 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27145869 #FOAvip English Wikipedia <ref>{{Cite pmid|27145869 }}</ref> In silico identification of anti-cancer compounds and plants from traditional Chinese medicine database #MMPMID27145869 Dai SX ; Li WX ; Han FF ; Guo YC ; Zheng JJ ; Liu JQ ; Wang Q ; Gao YD ; Li GH ; Huang JF Sci Rep 2016[May]; 6 (ä): 25462 PMID27145869 show ga There is a constant demand to develop new, effective, and affordable anti-cancer drugs. The traditional Chinese medicine (TCM) is a valuable and alternative resource for identifying novel anti-cancer agents. In this study, we aim to identify the anti-cancer compounds and plants from the TCM database by using cheminformatics. We first predicted 5278 anti-cancer compounds from TCM database. The top 346 compounds were highly potent active in the 60 cell lines test. Similarity analysis revealed that 75% of the 5278 compounds are highly similar to the approved anti-cancer drugs. Based on the predicted anti-cancer compounds, we identified 57 anti-cancer plants by activity enrichment. The identified plants are widely distributed in 46 genera and 28 families, which broadens the scope of the anti-cancer drug screening. Finally, we constructed a network of predicted anti-cancer plants and approved drugs based on the above results. The network highlighted the supportive role of the predicted plant in the development of anti-cancer drug and suggested different molecular anti-cancer mechanisms of the plants. Our study suggests that the predicted compounds and plants from TCM database offer an attractive starting point and a broader scope to mine for potential anti-cancer agents. |*Databases, Factual [MESH] |Antineoplastic Agents, Phytogenic/*chemistry/classification [MESH] |Computer Simulation [MESH] |Data Mining/*statistics & numerical data [MESH] |Drugs, Chinese Herbal/*chemistry/classification [MESH] |Humans [MESH] |Medicine, Chinese Traditional [MESH] |Neoplasms/drug therapy [MESH] |Plants, Medicinal/*chemistry/classification [MESH]In silico identification of anti-cancer compounds and plants from trad(epmc).pdf DeepDyve Pubget Overpricing