Drug repurposing for SARS-CoV-2: a high-throughput molecular docking, molecular dynamics, machine learning, and DFT study

A micro-molecule of dimension 125 nm has caused around 479 million human infections (80 M for the USA) and 6.1 million human deaths (977,000 for the USA) worldwide and slashed the global economy by US$ 8.5 Trillion over two years period. The only other events in recent history that caused comparative human life loss through direct usage (either by human or nature, respectively) of structure-property relations of 'nano-structures' (either human-made or nature, respectively) were nuclear bomb attacks during World War II and 1918 Flu Pandemic. This molecule is called SARS-CoV-2, which causes a disease known as COVID-19. The high liability cost of the pandemic had incentivized various private, government, and academic entities to work towards finding a cure for this and emerging diseases. As an outcome, multiple vaccine candidates are discovered to avoid the infection in the first place. But so far, there has been no success in finding fully effective therapeutic candidates. In this paper, we attempted to provide multiple therapy candidates based upon a sophisticated multi-scale in-silico framework, which increases the probability of the candidates surviving an in-vivo trial. We have selected a group of ligands from the ZINC database based upon previously partially successful candidates, i.e., Hydroxychloroquine, Lopinavir, Remdesivir, Ritonavir. We have used the following robust framework to screen the ligands; Step-I: high throughput molecular docking, Step-II: molecular dynamics analysis, Step-III: density functional theory analysis. In total, we have analyzed 242,000(ligands)*9(proteins) = 2.178 million unique protein binding site/ligand combinations. The proteins were selected based on recent experimental studies evaluating potential inhibitor binding sites. Step-I had filtered that number down to 10 ligands/protein based on molecular docking binding energy, further screening down to 2 ligands/protein based on drug-likeness analysis. Additionally, these two ligands per protein were analyzed in Step-II with a molecular dynamic modeling-based RMSD filter of less than 1Å. It finally suggested three ligands (ZINC001176619532, ZINC000517580540, ZINC000952855827) attacking different binding sites of the same protein(7BV2), which were further analyzed in Step-III to find the rationale behind comparatively higher ligand efficacy.