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Review

Lessons Learnt from COVID-19: Computational Strategies for Facing Present and Future Pandemics

Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Via Marzolo 5, 35131 Padova, Italy
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Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2023, 24(5), 4401; https://doi.org/10.3390/ijms24054401
Submission received: 27 January 2023 / Revised: 19 February 2023 / Accepted: 21 February 2023 / Published: 23 February 2023
(This article belongs to the Special Issue Molecular Interactions and Mechanisms of COVID-19 Inhibition 2.0)

Abstract

Since its outbreak in December 2019, the COVID-19 pandemic has caused the death of more than 6.5 million people around the world. The high transmissibility of its causative agent, the SARS-CoV-2 virus, coupled with its potentially lethal outcome, provoked a profound global economic and social crisis. The urgency of finding suitable pharmacological tools to tame the pandemic shed light on the ever-increasing importance of computer simulations in rationalizing and speeding up the design of new drugs, further stressing the need for developing quick and reliable methods to identify novel active molecules and characterize their mechanism of action. In the present work, we aim at providing the reader with a general overview of the COVID-19 pandemic, discussing the hallmarks in its management, from the initial attempts at drug repurposing to the commercialization of Paxlovid, the first orally available COVID-19 drug. Furthermore, we analyze and discuss the role of computer-aided drug discovery (CADD) techniques, especially those that fall in the structure-based drug design (SBDD) category, in facing present and future pandemics, by showcasing several successful examples of drug discovery campaigns where commonly used methods such as docking and molecular dynamics have been employed in the rational design of effective therapeutic entities against COVID-19.
Keywords: COVID-19; SARS-CoV-2; rational drug design; CADD; SBDD; homology modeling; docking; pharmacophore; protein–ligand interaction fingerprints; molecular dynamics COVID-19; SARS-CoV-2; rational drug design; CADD; SBDD; homology modeling; docking; pharmacophore; protein–ligand interaction fingerprints; molecular dynamics
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MDPI and ACS Style

Pavan, M.; Moro, S. Lessons Learnt from COVID-19: Computational Strategies for Facing Present and Future Pandemics. Int. J. Mol. Sci. 2023, 24, 4401. https://doi.org/10.3390/ijms24054401

AMA Style

Pavan M, Moro S. Lessons Learnt from COVID-19: Computational Strategies for Facing Present and Future Pandemics. International Journal of Molecular Sciences. 2023; 24(5):4401. https://doi.org/10.3390/ijms24054401

Chicago/Turabian Style

Pavan, Matteo, and Stefano Moro. 2023. "Lessons Learnt from COVID-19: Computational Strategies for Facing Present and Future Pandemics" International Journal of Molecular Sciences 24, no. 5: 4401. https://doi.org/10.3390/ijms24054401

APA Style

Pavan, M., & Moro, S. (2023). Lessons Learnt from COVID-19: Computational Strategies for Facing Present and Future Pandemics. International Journal of Molecular Sciences, 24(5), 4401. https://doi.org/10.3390/ijms24054401

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