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Improved And Optimized Drug Repurposing For The SARS-CoV-2 Pandemic

Sarel Cohen, Moshik Hershcovitch, Martin Taraz, Otto Kißig, View ORCID ProfileDavis Issac, Andrew Wood, Daniel Waddington, Peter Chin, Tobias Friedrich
doi: https://doi.org/10.1101/2022.03.24.485618
Sarel Cohen
1Hasso Plattner Institute, University Potsdam, Germany
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Moshik Hershcovitch
2IBM Research
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Martin Taraz
1Hasso Plattner Institute, University Potsdam, Germany
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Otto Kißig
1Hasso Plattner Institute, University Potsdam, Germany
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Davis Issac
1Hasso Plattner Institute, University Potsdam, Germany
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  • For correspondence: davisissac22@gmail.com
Andrew Wood
3Boston University
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Daniel Waddington
2IBM Research
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Peter Chin
3Boston University
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Tobias Friedrich
1Hasso Plattner Institute, University Potsdam, Germany
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Abstract

The active global SARS-CoV-2 pandemic caused more than 426 million cases and 5.8 million deaths worldwide. The development of completely new drugs for such a novel disease is a challenging, time intensive process. Despite researchers around the world working on this task, no effective treatments have been developed yet. This emphasizes the importance of drug repurposing, where treatments are found among existing drugs that are meant for different diseases. A common approach to this is based on knowledge graphs, that condense relationships between entities like drugs, diseases and genes. Graph neural networks (GNNs) can then be used for the task at hand by predicting links in such knowledge graphs. Expanding on state-of-the-art GNN research, Doshi et al. recently developed the Dr-COVID model. We further extend their work using additional output interpretation strategies. The best aggregation strategy derives a top-100 ranking of 8,070 candidate drugs, 32 of which are currently being tested in COVID-19-related clinical trials. Moreover, we present an alternative application for the model, the generation of additional candidates based on a given pre-selection of drug candidates using collaborative filtering. In addition, we improved the implementation of the Dr-COVID model by significantly shortening the inference and pre-processing time by exploiting data-parallelism. As drug repurposing is a task that requires high computation and memory resources, we further accelerate the post-processing phase using a new emerging hardware — we propose a new approach to leverage the use of high-capacity Non-Volatile Memory for aggregate drug ranking.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • {sarel.cohen{at}hpi.uni-potsdam.de, davis.issac{at}hpi.uni-potsdam.de, tobias.friedrich{at}hpi.uni-potsdam.de} {martin.taraz{at}student.hpi.de, otto.kissig{at}student.hpi.de}

  • moshikh{at}il.ibm.com, daniel.waddington{at}ibm.com

  • {aewood{at}bu.edu, spchin{at}bu.edu}

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted March 24, 2022.
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Improved And Optimized Drug Repurposing For The SARS-CoV-2 Pandemic
Sarel Cohen, Moshik Hershcovitch, Martin Taraz, Otto Kißig, Davis Issac, Andrew Wood, Daniel Waddington, Peter Chin, Tobias Friedrich
bioRxiv 2022.03.24.485618; doi: https://doi.org/10.1101/2022.03.24.485618
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Improved And Optimized Drug Repurposing For The SARS-CoV-2 Pandemic
Sarel Cohen, Moshik Hershcovitch, Martin Taraz, Otto Kißig, Davis Issac, Andrew Wood, Daniel Waddington, Peter Chin, Tobias Friedrich
bioRxiv 2022.03.24.485618; doi: https://doi.org/10.1101/2022.03.24.485618

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