Repurpose Open Data to Discover Therapeutics for COVID-19 Using Deep Learning

• Published in: Journal of Proteome Research Vol. 19; no. 11; pp. 4624 - 4636
• Main Authors: Zeng, Xiangxiang, Song, Xiang, Ma, Tengfei, Pan, Xiaoqin, Zhou, Yadi, Hou, Yuan, Zhang, Zheng, Li, Kenli, Karypis, George, Cheng, Feixiong
• Format: Journal Article Web Resource
• Language: English
• Published: United States American Chemical Society 06.11.2020
• Subjects: Antiviral Agents; Betacoronavirus; Coronavirus Infections - drug therapy; Coronavirus Infections - virology; COVID-19; Deep Learning; Drug Repositioning - methods; Humans; Pandemics; Pneumonia, Viral - drug therapy; Pneumonia, Viral - virology; Proteome; SARS-CoV-2; Transcriptome; COVID-19; deep learning; SARS-CoV-2; drug repurposing; knowledge graph; representation learning
• ISSN: 1535-3893; 1535-3907
• DOI: 10.1021/acs.jproteome.0c00316

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.