Advances in the computational landscape for repurposed drugs against COVID-19. Issue 12 (December 2021)

• Record Type: Journal Article
• Authors: Aronskyy, Illya; Masoudi-Sobhanzadeh, Yosef; Cappuccio, Antonio; Zaslavsky, Elena
• Is Part Of: Drug discovery today. Volume 26:Issue 12(2021)
• Page Start: 2800
• Page End: 2815
• Publication Date: 2021-12
• Subjects: Computational drug repurposing -- COVID-19 -- SARS-CoV-2 -- Docking and molecular dynamics -- Structure-guided machine learning -- Virus–host interaction network analysis; Drugs -- Design -- Periodicals; Drugs -- Research -- Periodicals; 615.1
• DOI: 10.1016/j.drudis.2021.07.026
• Languages: English
• ISSNs: 1359-6446

Abstract

Highlights: Computational efforts to repurpose drugs for use against COVID-19 have made important discoveries. Methods include molecular docking and machine learning, as well as network-based, text and hybrid approaches. Predicted mechanisms include inhibition of viral proteins or disruption of host pathway pathogenesis. SARS-CoV-2 data portals and other repositories will aid in future research. Potential future directions include combination therapy and personalized treatments. Abstract: The COVID-19 pandemic has caused millions of deaths and massive societal distress worldwide. Therapeutic solutions are urgently needed, but de novo drug development remains a lengthy process. One promising alternative is computational drug repurposing, which enables the prioritization of existing compounds through fast in silico analyses. Recent efforts based on molecular docking, machine learning, and network analysis have produced actionable predictions. Some predicted drugs, targeting viral proteins and pathological host pathways are undergoing clinical trials. Here, we review this work, highlight drugs with high predicted efficacy and classify their mechanisms of action. We discuss the strengths and limitations of the published methodologies and outline possible future directions. Finally, we curate a list of COVID-19 data portals and other repositories that could be used to accelerate future research.