  A molecule of dimension 125nm has caused around 479 Million human infections
(80M for the USA) & 6.1 Million human deaths (977,000 for the USA) worldwide
and slashed the global economy by US$ 8.5 Trillion over two years. The only
other events in recent history that caused comparative human life loss through
direct usage (either by (wo)man or nature, respectively) of structure-property
relations of 'nano-structures' (either (wo)man-made or nature, respectively)
were nuclear bomb attacks of Japanese cities by the USA during World War II and
1918 Flu Pandemic. This molecule is 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
these & emerging diseases. As result, multiple vaccine candidates are
discovered to avoid the infection in first place. But so far, there has been no
success in finding fully effective therapeutics candidates. In this paper, we
attempted to provide multiple therapy candidates based upon a sophisticated
multi-scale in-silico framework. We have used the following robust framework to
screen the ligands; Step-I: high throughput docking, Step-II: molecular
dynamics, Step-III: density functional theory analysis. In total, we have
analyzed 2.2 Million unique protein binding site/ligand combinations. The
proteins were selected based on recent experimental studies. 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/proteins were
investigated in Step-II with a molecular dynamic based RMSD analysis. It
finally suggested three ligands (ZINC1176619532, ZINC517580540, ZINC952855827)
attacking different binding sites of the protein(7BV2), which were further
analyzed in Step III.
