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

TL;DR

This paper presents a hybrid machine learning and physics-based computational infrastructure leveraging supercomputers to accelerate COVID-19 drug discovery by efficiently screening potential antiviral compounds.

Contribution

It introduces an innovative infrastructure combining machine learning and physics-based methods for high-throughput drug screening on supercomputers.

Findings

Successfully demonstrated workflow for COVID-19 target proteins

Achieved large-scale calculations for drug repurposing

Identified potential antiviral lead compounds

Abstract

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.