  The race to meet the challenges of the global pandemic has served as a
reminder that the existing drug discovery process is expensive, inefficient and
slow. There is a major bottleneck screening the vast number of potential small
molecules to shortlist lead compounds for antiviral drug development. New
opportunities to accelerate drug discovery lie at the interface between machine
learning methods, in this case developed for linear accelerators, and
physics-based methods. The two in silico methods, each have their own
advantages and limitations which, interestingly, complement each other. Here,
we present an innovative method that combines both approaches to accelerate
drug discovery. The scale of the resulting workflow is such that it is
dependent on high performance computing. We have demonstrated the applicability
of this workflow on four COVID-19 target proteins and our ability to perform
the required large-scale calculations to identify lead compounds on a variety
of supercomputers.
