  COVID-19 is a devastating respiratory and inflammatory illness caused by a
new coronavirus that is rapidly spreading throughout the human population. Over
the past 6 months, severe acute respiratory syndrome coronavirus 2
(SARS-CoV-2), the virus responsible for COVID-19, has already infected over
11.6 million (25% located in United States) and killed more than 540K people
around the world. As we face one of the most challenging times in our recent
history, there is an urgent need to identify drug candidates that can attack
SARS-CoV-2 on multiple fronts. We have therefore initiated a computational
dynamics drug pipeline using molecular modeling, structure simulation, docking
and machine learning models to predict the inhibitory activity of several
million compounds against two essential SARS-CoV-2 viral proteins and their
host protein interactors; S/Ace2, Tmprss2, Cathepsins L and K, and Mpro to
prevent binding, membrane fusion and replication of the virus, respectively.
All together we generated an ensemble of structural conformations that increase
high quality docking outcomes to screen over >6 million compounds including all
FDA-approved drugs, drugs under clinical trial (>3000) and an additional >30
million selected chemotypes from fragment libraries. Our results yielded an
initial set of 350 high value compounds from both new and FDA-approved
compounds that can now be tested experimentally in appropriate biological model
systems. We anticipate that our results will initiate screening campaigns and
accelerate the discovery of COVID-19 treatments.
