Repurpose Open Data to Discover Therapeutics for COVID-19 Using Deep Learning.
J Proteome Res
; 19(11): 4624-4636, 2020 11 06.
Article
in English
| MEDLINE | ID: covidwho-960269
ABSTRACT
There have been more than 2.2 million confirmed cases and over 120â¯000 deaths from the human coronavirus disease 2019 (COVID-19) pandemic, caused by the novel severe acute respiratory syndrome coronavirus (SARS-CoV-2), in the United States alone. However, there is currently a lack of proven effective medications against COVID-19. Drug repurposing offers a promising route for the development of prevention and treatment strategies for COVID-19. This study reports an integrative, network-based deep-learning methodology to identify repurposable drugs for COVID-19 (termed CoV-KGE). Specifically, we built a comprehensive knowledge graph that includes 15 million edges across 39 types of relationships connecting drugs, diseases, proteins/genes, pathways, and expression from a large scientific corpus of 24 million PubMed publications. Using Amazon's AWS computing resources and a network-based, deep-learning framework, we identified 41 repurposable drugs (including dexamethasone, indomethacin, niclosamide, and toremifene) whose therapeutic associations with COVID-19 were validated by transcriptomic and proteomics data in SARS-CoV-2-infected human cells and data from ongoing clinical trials. Whereas this study by no means recommends specific drugs, it demonstrates a powerful deep-learning methodology to prioritize existing drugs for further investigation, which holds the potential to accelerate therapeutic development for COVID-19.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Pneumonia, Viral
/
Coronavirus Infections
/
Drug Repositioning
/
Pandemics
/
Betacoronavirus
/
Deep Learning
Type of study:
Prognostic study
/
Reviews
Limits:
Humans
Language:
English
Journal:
J Proteome Res
Journal subject:
Biochemistry
Year:
2020
Document Type:
Article
Affiliation country:
Acs.jproteome.0c00316
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