Identifying Promising Antiviral Drug Candidates Against SARS-CoV-2 Using Computational Drug Repurposing Methodology

Overview

Responsible for over 460 million infections and 6 million deaths as of March 2022, the COVID-19 pandemic has disrupted the livelihoods of people across the globe. Research and development have contributed to effective vaccines, widespread safety protocols have aided in mitigating the spread of the virus, and drug discovery has decreased mortality. However, the demand for effective COVID-19 treatments is still significant. In this study to identify promising drug candidates for COVID-19, we focused on inhibition of the viral main protease (nsp5), which is a key enzyme in the viral replication cycle. Using a computer-aided drug discovery approach, we repurposed Beclabuvir, an investigational drug used in the treatment of chronic Hepatitis C. As a seed compound in previous studies, Beclabuvir has been shown to have a high binding affinity with nsp5; however, a large molecular weight paired with low water solubility renders this compound inefficient as a drug. We created a compound library based on structural similarity with Beclabuvir and screened thirty analogs for drug likeness using the Swiss ADME tool. Analogs with promising ADMET properties were then virtually docked with the structural model of the nsp5 (PDB-ID 6lu7) protein to determine binding affinities. The results of this study revealed three analogs of Beclabuvir with comparably high binding affinities with nsp5, thereby providing evidence for three potentially effective drugs against SARS-CoV-2.