Network Medicine Framework for Identifying Drug Repurposing Opportunities for COVID-19
Network Medicine Framework for Identifying Drug Repurposing Opportunities for COVID-19 Deisy Morselli Gysi1,2,*, Ítalo Do Valle1,*, Marinka Zitnik3,4,*, Asher Ameli5,6,*, Xiao Gan1,2,*, Onur Varol1,7, Susan Dina Ghiassian5, JJ Patten8, Robert Davey8, Joseph Loscalzo9, and Albert-László Barabási1,10 1 Network Science Institute and Department of Physics, Northeastern University, Boston, MA 02115, USA;2 Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA; 3 Department of Biomedical Informatics, Harvard University, Boston, MA 02115, USA; 4Harvard Data Science Initiative, Harvard University, Cambridge, MA 02138, USA.5 Scipher Medicine, 221 Crescent St, Suite 103A, Waltham, MA 02453; 6 Department of Physics, Northeastern University, Boston, MA 02115, USA; 7 Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul 34956, Turkey; 8 Department of microbiology, NEIDL, Boston University, Boston, MA, USA; 9 Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA; 10 Department of Network and Data Science, Central European University, Budapest 1051, Hungary. *Those authors contributed equally Abstract The current pandemic has highlighted the need for methodologies that can quickly and reliably prioritize clinically approved compounds for their potential effectiveness for SARS-CoV-2 infections. In the past decade, network medicine has developed and validated multiple predictive algorithms for drug repurposing, exploiting the sub-cellular network-based relationship between a drug’s targets and disease genes. Here, we deployed algorithms relying on artificial intelligence, network diffusion, and network proximity, tasking each of them to rank 6,340 drugs for their expected efficacy against SARS-CoV-2.
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