  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 infrastructural development that combines both
approaches to accelerate drug discovery. The scale of the potential resulting
workflow is such that it is dependent on supercomputing to achieve extremely
high throughput. We have demonstrated the viability of this workflow for the
study of inhibitors for four COVID-19 target proteins and our ability to
perform the required large-scale calculations to identify lead antiviral
compounds through repurposing on a variety of supercomputers.
