In silico identification of anti-cancer compounds and plants from traditional Chinese medicine database View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2016-07

AUTHORS

Shao-Xing Dai, Wen-Xing Li, Fei-Fei Han, Yi-Cheng Guo, Jun-Juan Zheng, Jia-Qian Liu, Qian Wang, Yue-Dong Gao, Gong-Hua Li, Jing-Fei Huang

ABSTRACT

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. More... »

PAGES

25462

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/srep25462

DOI

http://dx.doi.org/10.1038/srep25462

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1021420970

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/27145869


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