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      Targeting infectious diseases: The CoVex example

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            Abstract

            In a pandemic, such as the one caused by the coronavirus in 2020, fast action is required to provide insights into the disease mechanisms and to find potential drug targets to slow the progress of the disease and lower mortality. We developed CoVex, an interactive online platform for the exploration of the SARS-CoV-2 host interactome and the identification of drug candidates. CoVex integrates the virus-human protein interactions, human protein-protein interactions, and drug-target interactions, and allows for visual exploration of the data. The exploration is guided by network-based systems medicine algorithms that enable the assembly of ranked lists of candidates for already approved drugs. Unlike novel drugs, approved drugs have the advantage that most of their side effects and contraindications are known. Additionally, they are already available and can be distributed quickly, which makes them promising candidates to stop the uncontrolled spread of the disease early. CoVex is an example of how this could be achieved in future pandemics and provides valuable lessons on what is required in such a situation to maximize the potential of computational approaches that can significantly speed up drug discovery.

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            Author and article information

            Conference
            ScienceOpen
            26 August 2022
            Affiliations
            [1 ] Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Munich (Germany)
            [2 ] Institute for Computational Systems Biology, University of Hamburg, Hamburg (Germany)
            [3 ] Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen (Germany)
            [4 ] Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics of Technische Universität Braunschweig and Hannover Medical School, Braunschweig (Germany)
            [5 ] Braunschweig Integrated Centre of Systems Biology (BRICS), TU Braunschweig, Braunschweig (Germany) 6 Technical University of Munich, Garching (Germany
            [6 ] Technical University of Munich, Garching (Germany)
            [7 ] Institute of Virology, TUM School of Medicine, Technical University of Munich, München (Germany)
            [8 ] Natural Sciences Department, Universidad Autónoma Metropolitana-Cuajimalpa (UAM-C), 05300, Mexico City (Mexico)
            Article
            10.14293/S2199-rexpo22008.v1
            cd45a34a-c654-4cb0-9c38-14635502d301
            The Authors

            Published under Creative Commons Attribution 4.0 International ( CC BY 4.0). Users are allowed to share (copy and redistribute the material in any medium or format) and adapt (remix, transform, and build upon the material for any purpose, even commercially), as long as the authors and the publisher are explicitly identified and properly acknowledged as the original source.

            RExPO22
            Maastricht, Netherlands
            2-3 September, 2022
            History
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            ScienceOpen


            Data sharing not applicable to this article as no datasets were generated or analysed during the current study.
            Covid-19,SARS-CoV-2,Computational drug repurposing,Graph algorithms,Virus-host interactome,Web tool

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