TY - JOUR
T1 - Repurpose Open Data to Discover Therapeutics for COVID-19 Using Deep Learning
AU - Zeng, Xiangxiang
AU - Song, Xiang
AU - Ma, Tengfei
AU - Pan, Xiaoqin
AU - Zhou, Yadi
AU - Hou, Yuan
AU - Zhang, Zheng
AU - Li, Kenli
AU - Karypis, George
AU - Cheng, Feixiong
N1 - Publisher Copyright:
© 2020 American Chemical Society. All rights reserved.
PY - 2020/11/6
Y1 - 2020/11/6
N2 - There have been more than 2.2 million confirmed cases and over 120 »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.
AB - There have been more than 2.2 million confirmed cases and over 120 »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.
KW - COVID-19
KW - SARS-CoV-2
KW - deep learning
KW - drug repurposing
KW - knowledge graph
KW - representation learning
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U2 - 10.1021/acs.jproteome.0c00316
DO - 10.1021/acs.jproteome.0c00316
M3 - Article
C2 - 32654489
AN - SCOPUS:85090928884
SN - 1535-3893
VL - 19
SP - 4624
EP - 4636
JO - Journal of Proteome Research
JF - Journal of Proteome Research
IS - 11
ER -