| Literature DB >> 24980786 |
Mark Minie1, Gaurav Chopra2, Geetika Sethi3, Jeremy Horst4, George White3, Ambrish Roy5, Kaushik Hatti6, Ram Samudrala7.
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.Entities:
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Year: 2014 PMID: 24980786 PMCID: PMC4167471 DOI: 10.1016/j.drudis.2014.06.018
Source DB: PubMed Journal: Drug Discov Today ISSN: 1359-6446 Impact factor: 7.851