Using Bayesian Optimization to Accelerate Virtual Screening for the Discovery of Therapeutics Appropriate for Repurposing for COVID-19
This work addresses the urgent need for rapid therapeutic discovery during the COVID-19 pandemic, though it is incremental as it builds on existing high-throughput virtual screening methods.
The paper tackled the problem of accelerating virtual screening for COVID-19 drug repurposing by using Bayesian optimization to prioritize calculations, resulting in faster identification of high-performing candidates and expanding the utility of high-performance computing systems for time-critical screening.
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