Estudo do Docking e da Dinâmica Molecular dos principais Inibidores da Glicoproteína Spike do Sars-Cov-2 através de inteligência artificial

Abstract

At the end of 2019, SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus-2) was the cause of unknown pneumonia, but soon after, it was portrayed as coronavirus 2019 (COVID-19). COVID-19 is an ongoing global pandemic that has claimed and threatened many lives worldwide. Before developing immune agents to combat SARS-CoV-2, potential screenings with numerous known drugs were previously carried out. Still, the efficacy to combat the critical problems was not satisfactory. Soon after, due to its potential for replication and infection, SARS-CoV-2 presented new variants, the result of an accumulation of mutations in the sequence and structure of the Spike (S) glycoprotein, which is by far the most critical protein for SARS-CoV-2. This protein participates directly in the replication of SARS-CoV-2 infection, pathogenicity, transmission, and evolution. In their mutation, they can carry the new variants that generally have the potential to be more transmissible and, therefore, can attenuate the immunity induced by the immunizers. The objective of this work was to theoretically find the main inhibitors of S glycoprotein of SARS-CoV-2, using the methodology of drug design De Novo, in the search for new antiviral agents, with the help of the MolAiCal computational package, which used machine learning and artificial intelligence.