  The COVID-19 pandemic has led to unprecedented efforts to identify drugs that
can reduce its associated morbidity/mortality rate. Computational chemistry
approaches hold the potential for triaging potential candidates far more
quickly than their experimental counterparts. These methods have been widely
used to search for small molecules that can inhibit critical proteins involved
in the SARS-CoV-2 replication cycle. An important target is the SARS-CoV-2 main
protease Mpro, an enzyme that cleaves the viral polyproteins into individual
proteins required for viral replication and transcription. Unfortunately,
standard computational screening methods face difficulties in ranking diverse
ligands to a receptor due to disparate ligand scaffolds and varying charge
states. Here, we describe full density functional quantum mechanical (DFT/QM)
simulations of Mpro in complex with various ligands to obtain absolute ligand
binding energies. Our calculations are enabled by a new cloud-native parallel
DFT/QM implementation running on computational resources from Amazon Web
Services (AWS). The results we obtain are promising: the approach is quite
capable of scoring a very diverse set of existing drug compounds for their
affinities to Mpro and suggest the DFT/QM approach is potentially more broadly
applicable to repurpose screening against this target. In addition, each DFT/QM
simulation required only ~1 hour (wall clock time) per ligand. The fast
turnaround time raises the practical possibility of a broad application of
large-scale quantum mechanics in the drug discovery pipeline at stages where
ligand diversity is essential.
