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The race to meet the challenges of the global pandemic has served as areminder that the existing drug discovery process is expensive, inefficient andslow. There is a major bottleneck screening the vast number of potential smallmolecules to shortlist lead compounds for antiviral drug development. Newopportunities to accelerate drug discovery lie at the interface between machinelearning methods, in this case developed for linear accelerators, andphysics-based methods. The two in silico methods, each have their ownadvantages and limitations which, interestingly, complement each other. Here,we present an innovative infrastructural development that combines bothapproaches to accelerate drug discovery. The scale of the potential resultingworkflow is such that it is dependent on supercomputing to achieve extremelyhigh throughput. We have demonstrated the viability of this workflow for thestudy of inhibitors for four COVID-19 target proteins and our ability toperform the required large-scale calculations to identify lead antiviralcompounds through repurposing on a variety of supercomputers.

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Pandemic Drugs at Pandemic Speed: Infrastructure for Accelerating  COVID-19 Drug Discovery with Hybrid Machine Learning- and Physics-based  Simulations on High Performance Computers