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

arXiv: https://arxiv.org/abs/2005.07121 · HTML: https://mlsanity.com/paper/2005.07121/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 May 2020.

## Built on by (most cited first, 3 of 3)

- [GAUCHE: A Library for Gaussian Processes in Chemistry](https://mlsanity.com/paper/2212.04450/) (2022)
- [Self-focusing virtual screening with active design space pruning](https://mlsanity.com/paper/2205.01753/) (2022)
- [Applications of Gaussian Processes at Extreme Lengthscales: From Molecules to Black Holes](https://mlsanity.com/paper/2303.14291/) (2023)

## Abstract (arXiv)

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