Computational Modeling of Protease Inhibitors for the Development of Drugs Against Coronaviruses

In late 2019, SARS-CoV-2, a new coronavirus, emerged from Wuhan, China, and caused a world pandemic in a few months. There are no US Food and Drugs Administration (USFDA)-approved antiviral drugs or vaccines that could be used to prevent or treat this viral infection. However, several clinical trials are ongoing, searching for therapeutic alternatives. As time is crucial in a pandemic, the scientific community has used drug repurposing and data obtained from in silico models to identify possible lead compound inhibitors of SARS-CoV-2. Direct-acting antivirals are the most promising tools to control viral infections. One of the targets for direct-acting antivirals is the main protease (Mpro), a key enzyme in the SARS-CoV-2 replication cycle. This protease is a cysteine protease that shares high homology with the SARS-CoV Mpro and could be susceptible to blocking its activity by compounds such as HIV protease inhibitors. In this book chapter, we focused on the structural features of the SARS-CoV-2 Mpro that could be applied to developing new therapies using computational aid, although it is not certain whether the outcomes of such computational studies will immediately result in effective anti-SARS-CoV-2 therapy.