Computational anti-COVID-19 drug design: progress and challenges.

Jinxian Wang, Ying Zhang, Wenjuan Nie, Yi Luo, Lei Deng

Briefings in bioinformatics 2022 Jan 17

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Vaccines have made gratifying progress in preventing the 2019 coronavirus disease (COVID-19) pandemic. However, the emergence of variants, especially the latest delta variant, has brought considerable challenges to human health. Hence, the development of robust therapeutic approaches, such as anti-COVID-19 drug design, could aid in managing the pandemic more efficiently. Some drug design strategies have been successfully applied during the COVID-19 pandemic to create and validate related lead drugs. The computational drug design methods used for COVID-19 can be roughly divided into (i) structure-based approaches and (ii) artificial intelligence (AI)-based approaches. Structure-based approaches investigate different molecular fragments and functional groups through lead drugs and apply relevant tools to produce antiviral drugs. AI-based approaches usually use end-to-end learning to explore a larger biochemical space to design antiviral drugs. This review provides an overview of the two design strategies of anti-COVID-19 drugs, the advantages and disadvantages of these strategies and discussions of future developments. © The Author(s) 2021. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Citation

Jinxian Wang, Ying Zhang, Wenjuan Nie, Yi Luo, Lei Deng. Computational anti-COVID-19 drug design: progress and challenges. Briefings in bioinformatics. 2022 Jan 17;23(1)