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CANDO与无限的药物发现前沿领域。

CANDO and the infinite drug discovery frontier.

作者信息

Minie Mark, Chopra Gaurav, Sethi Geetika, Horst Jeremy, White George, Roy Ambrish, Hatti Kaushik, Samudrala Ram

机构信息

University of Washington, Department of Bioengineering, Seattle, WA 98109, United States.

University of Washington, Department of Microbiology, Seattle, WA 98109, United States; University of California, San Francisco, Diabetes Center, San Francisco, CA 94143, United States.

出版信息

Drug Discov Today. 2014 Sep;19(9):1353-63. doi: 10.1016/j.drudis.2014.06.018. Epub 2014 Jun 26.

Abstract

The Computational Analysis of Novel Drug Opportunities (CANDO) platform (http://protinfo.org/cando) uses similarity of compound-proteome interaction signatures to infer homology of compound/drug behavior. We constructed interaction signatures for 3733 human ingestible compounds covering 48,278 protein structures mapping to 2030 indications based on basic science methodologies to predict and analyze protein structure, function, and interactions developed by us and others. Our signature comparison and ranking approach yielded benchmarking accuracies of 12-25% for 1439 indications with at least two approved compounds. We prospectively validated 49/82 'high value' predictions from nine studies covering seven indications, with comparable or better activity to existing drugs, which serve as novel repurposed therapeutics. Our approach may be generalized to compounds beyond those approved by the FDA, and can also consider mutations in protein structures to enable personalization. Our platform provides a holistic multiscale modeling framework of complex atomic, molecular, and physiological systems with broader applications in medicine and engineering.

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc9f/4167471/0ed6c567e741/nihms614462f1.jpg

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