The recent pandemic due to SARS-CoV-2, the last isolated human betacoronavirus,
has revolutionized modern knowledge of the pathogenesis of viral pneumonia.
The lack of specific antiviral drugs and the need to develop adequate research for new antiviral
drugs capable of treating this new form of the disease undertook three different research
paths quickly. The first one is aimed to test antiviral molecules already present in
therapeutic use, with a mechanism of action directed towards viral proteins functional to
replication or adsorption; the second one, it is the repositioning of molecules with known
pharmacological activity for which various chemistry studies have been prepared in an attempt
to find new and specific viral targets; the third, it is the search for molecules of natural
origin for which to demonstrate a specific anti-coronavirus activity. Many databases of
natural and synthetic substances have been used for the identification of potent inhibitors of
various viral targets. The field of computer-aided drug design seems to be promising and
useful for the identification of SARS-CoV-2 inhibitors; hence, different structure- and ligand-
based computational approaches have been used for their identification. This review
analyzes in-depth and critically the most recent publications in the field of applied computational
chemistry to find out molecules of natural origin with potent antiviral activity. Furthermore,
a critical and functional selection of some molecules with the best hypothetical
anti-SARS-CoV-2 activity is made for further studies by biological tests.
Keywords: SARS-CoV-2, Computational chemistry, Natural compounds inhibitors, natural compounds for covid,
Cross-Fertilization, Molecular Modeling Data.
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