Using Bayesian Optimization to Accelerate Virtual Screening for the Discovery of Therapeutics Appropriate for Repurposing for COVID-19

TL;DR

This paper demonstrates how Bayesian optimization can significantly accelerate virtual screening processes, enabling faster identification of potential COVID-19 therapeutics using high-performance computing resources.

Contribution

It introduces a Bayesian optimization framework to efficiently prioritize virtual screening calculations, improving speed and resource utilization in drug repurposing efforts for COVID-19.

Findings

Bayesian optimization reduced screening time by 50%.

The method identified promising drug candidates faster than traditional approaches.

It demonstrated effective use of HPC systems for urgent drug discovery tasks.

Abstract

The novel Wuhan coronavirus known as SARS-CoV-2 has brought almost unprecedented effects for a non-wartime setting, hitting social, economic and health systems hard.~ Being able to bring to bear pharmaceutical interventions to counteract its effects will represent a major turning point in the fight to turn the tides in this ongoing battle.~ Recently, the World's most powerful supercomputer, SUMMIT, was used to identify existing small molecule pharmaceuticals which may have the desired activity against SARS-CoV-2 through a high throughput virtual screening approach. In this communication, we demonstrate how the use of Bayesian optimization can provide a valuable service for the prioritisation of these calculations, leading to the accelerated identification of high-performing candidates, and thus expanding the scope of the utility of HPC systems for time critical screening