Abstract

Since there is currently no approved vaccine or small molecule therapeutic on the market, the recent global outbreak of SARS-CoV-2, which has nearly 15 million infected people and more than 600,000 fatalities (accessed on 20th July 2020), poses a significant challenge to all nations and societies. Nearly 1500 investigations are actively enrolling patients by invitation for clinical research against COVID-19 because to the urgent need for a causative therapy

The main protease of Coronaviruses, chymotrypsin-like protease (3CLpro), processes the large polyprotein 1ab releasing several further enzymes that are crucial for viral replication. Moreover, 3CLpro is unique for Coronaviruses and not found in higher organisms. This predestines 3CLpro as most attractive target for the development of antiinfective agents against SARS-CoV-2 and related Coronaviruses [1]. Consequently, several inhibitors of 3CLpro were developed mostly during the last 17 years right after the first wave of infection caused by the SARS-CoV-1. However, no experimental compound was developed further and reached the market. Most compounds were designed as covalent inactivators that react with the catalytic Cys145.

These guidelines, which are sometimes referred to as features, are made up of spheres where a heavy ligand atom with specific properties, such as hydrogen donor or acceptor properties, is meant to be positioned in order to make a specific interaction with a neighbouring amino acid. It is best to simulate the binding pocket using a sequence of overlapping excluded volume spheres in order to reduce the likelihood of receiving an excessive number of false positive hits for branched or bulky molecules that meet the requirements but would interfere with the receptor protein. These were produced utilising a radius of 2 from particular pocket amino acids [2]. Hits are not counted for molecules whose structures conflict with those of the excluded volume spheres. Using a pharmacophore model, one Very quick search for hits that satisfy all or some of the feature requirements across big 3D structure databases. The search process should very effectively enrich those chemical entities that provide high docking scores and have a higher likelihood of interacting with the relevant target protein if the pharmacophore model is adequately described. The MOE pharmacophore editor was used to launch the pharmacophore search [3]. All of the hits from the pharmacophore search were put through the previously mentioned follow-up virtual screening process using MOE to evaluate their binding affinity.