Looking for SARS-CoV-2 Therapeutics Through Computational Approaches

In the last few years, in silico tools, including drug repurposing coupled with structure-based virtual screening, have been extensively employed to look for anti-COVID-19 agents.

The present review aims to provide readers with a portrayal of computational approaches that could be conducted more quickly and cheaply to novel anti-viral agents. Particular attention is given to docking-based virtual screening.

The World Health Organization website was consulted to gain the latest information on SARS-CoV-2, its novel variants and their interplay with COVID-19 severity and treatment options. The Protein Data Bank was explored to look for 3D coordinates of SARS-CoV-2 proteins in their free and bound states, in the wild-types and mutated forms. Recent literature related to in silico studies focused on SARS-CoV-2 proteins was searched through PubMed.

A large amount of work has been devoted thus far to computationally targeting viral entry and searching for inhibitors of the S-protein/ACE2 receptor complex. Another large area of investigation is linked to in silico identification of molecules able to block viral proteases -including Mpro- thus avoiding maturation of proteins crucial for virus life cycle. Such computational studies have explored the inhibitory potential of the most diverse molecule databases (including plant extracts, dietary compounds, FDA approved drugs).

More efforts need to be dedicated in the close future to experimentally validate the therapeutic power of in silico identified compounds in order to catch, among the wide ensemble of computational hits, novel therapeutics to prevent and/or treat COVID- 19.