Chapter 7 - In silico application of data science, genomics, and bioinformatics in screening drug candidates against COVID-19

Abstract

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), is a recent pandemic that originated in the Chinese city of Wuhan, causing a global catastrophe that has affected more than 200 nations. Previously, no specific antiviral drug had been verified, making dealing with the current situation more challenging. As a result, it is critical to find an antiviral drug to treat COVID-19 as soon as feasible. The main objective of this study was to use bioinformatics tools to assess the efficacy of different phytochemicals against COVID-19. In this study, potential drug candidates against COVID-19 were identified using bioinformatics, a widely known discipline of data science and frequently utilized tool for genomics. The Autodock 4.2 molecular docking software was used in this study to examine the binding energy of 100 phytochemicals against COVID-19 main protease (Protein Data Bank ID-6LU7). According to the results of molecular docking, the compounds with significant binding energies were subjected to drug likeness and toxicity testing using Molinspiration and pkCSM online tools. From the results it was observed that the compounds glabranin and anthocyanin belonging to the flavonoid group had promising binding energy, drug likeness capability, and lower toxicity, making them potential COVID-19 drug candidates. The current study could therefore reveal that glabranin and anthocyanin compounds could be promising drug candidates against COVID-19 with the help of bioinformatics tools used in the field of data science and genomics. However, further evaluation of the stability of these compounds and/or complexes, such as in vitro and in vivo research, is required to confirm their therapeutic potential against COVID-19.