Drug repurposing prediction for COVID-19 using probabilistic networks and crowdsourced curation

David J. Skelton, Aoesha Alsobhe, Elisa Anastasi, Christian Atallah, Jasmine E. Bird, Bradley Brown, Dwayne Didon, Phoenix Gater, Katherine James, David D. Lennon Jr, James McLaughlin, Pollyanna E. J. Moreland, Matthew Pocock, Caroline J. Whitaker, Anil Wipat*

*Corresponding author for this work

Research output: Working paperPreprint

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Abstract

Severe acute respiratory syndrome coronavirus two (SARS-CoV-2), the virus responsible for the coronavirus disease 2019 (COVID-19) pandemic, represents an unprecedented global health challenge. Consequently, a large amount of research into the disease pathogenesis and potential treatments has been carried out in a short time frame. However, developing novel drugs is a costly and lengthy process, and is unlikely to deliver a timely treatment for the pandemic. Drug repurposing, by contrast, provides an attractive alternative, as existing drugs have already undergone many of the regulatory requirements. In this work we used a combination of network algorithms and human curation to search integrated knowledge graphs, identifying drug repurposing opportunities for COVID-19. We demonstrate the value of this approach, reporting on eight potential repurposing opportunities identified, and discuss how this approach could be incorporated into future studies.
Original languageEnglish
Number of pages30
DOIs
Publication statusSubmitted - 22 May 2020

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