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A critical overview of computational approaches employed for COVID-19 drug discovery

期刊

CHEMICAL SOCIETY REVIEWS
卷 50, 期 16, 页码 9121-9151

出版社

ROYAL SOC CHEMISTRY
DOI: 10.1039/d0cs01065k

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资金

  1. NIH [U24 CA224370, U24 TR002278, U01CA239108, U01CA207160]
  2. NSF RAPID [MCB-2032054]
  3. UCSD Moores Cancer Center 2020 SARS-CoV-2 seed grant
  4. RCSA COVID Initiative [27350]
  5. DGAPA (UNAM) [IV200121]
  6. NSF [CHE-1802789, CHE-2041108]
  7. DSF Charitable Foundation
  8. COVID-19 HPC Consortium
  9. Swiss National Science Foundation [205321_182176]
  10. Russian Foundation of Basic Research [20-04-60285]
  11. Intramural Research Program of the National Center for Advancing Translational Sciences, National Institutes of Health
  12. NIH GM [132826]
  13. [R44GM122196-02A1]

向作者/读者索取更多资源

COVID-19 has led to a large number of infections and deaths globally, causing severe disruptions to societies and economies. Extensive experimental and computational research efforts have been made to quickly develop diagnostics, vaccines, and drugs to combat the pandemic. Although drug repurposing has not provided rapid and global solutions, known drugs have been used in clinical settings and new clinical candidates are being considered in trials. Truly impactful computational tools that provide testable hypotheses and rapid sharing of research results are crucial for the development of much-needed therapeutics for COVID-19.
COVID-19 has resulted in huge numbers of infections and deaths worldwide and brought the most severe disruptions to societies and economies since the Great Depression. Massive experimental and computational research effort to understand and characterize the disease and rapidly develop diagnostics, vaccines, and drugs has emerged in response to this devastating pandemic and more than 130 000 COVID-19-related research papers have been published in peer-reviewed journals or deposited in preprint servers. Much of the research effort has focused on the discovery of novel drug candidates or repurposing of existing drugs against COVID-19, and many such projects have been either exclusively computational or computer-aided experimental studies. Herein, we provide an expert overview of the key computational methods and their applications for the discovery of COVID-19 small-molecule therapeutics that have been reported in the research literature. We further outline that, after the first year the COVID-19 pandemic, it appears that drug repurposing has not produced rapid and global solutions. However, several known drugs have been used in the clinic to cure COVID-19 patients, and a few repurposed drugs continue to be considered in clinical trials, along with several novel clinical candidates. We posit that truly impactful computational tools must deliver actionable, experimentally testable hypotheses enabling the discovery of novel drugs and drug combinations, and that open science and rapid sharing of research results are critical to accelerate the development of novel, much needed therapeutics for COVID-19.

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