In silico identification of anti-cancer compounds and plants from traditional Chinese medicine database
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.
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
State Key Laboratory of Genetic Resources and Evolution,Kunming Institute of Zoology,Chinese Academy State Key Laboratory of Genetic Resources and Evolution,Kunming Institute of Zoology,Chinese Academy State Key Laboratory of Genetic Resources and Evolution,Kunming Institute of Zoology,Chinese Academy Kunming Biological Diversity Regional Center of Instruments,Kunming Institute of Zoology,Chinese Aca State Key Laboratory of Genetic Resources and Evolution,Kunming Institute of Zoology,Chinese Academy
国内会议
大连
英文
417-425
2016-09-24（万方平台首次上网日期，不代表论文的发表时间）