The recent pandemic due to SARS-CoV-2, the last isolated human beta-coronavirus, 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 an 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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