Pandemic drugs at pandemic speed: infrastructure for accelerating COVID-19 drug discovery with hybrid machine learning- and physics-based simulations on high-performance computers
Resource type
Journal article
Creator (person)
Bhati, Agastya P.
Wan, Shunzhou
Alfè, Dario
Clyde, Austin R.
Bode, Mathis
Tan, Li
Titov, Mikhail
Merzky, Andre
Turilli, Matteo
Jha, Shantenu
Highfield, Roger R.
Rocchia, Walter
Scafuri, Nicola
Succi, Sauro
Kranzlmà¼ller, Dieter
Mathias, Gerald
Wifling, David
Donon, Yann
Di Meglio, Alberto
Vallecorsa, Sofia
Ma, Heng
Trifan, Anda
Ramanathan, Arvind
Brettin, Tom
Partin, Alexander
Xia, Fangfang
Duan, Xiaotan
Stevens, Rick
Coveney, Peter V.
Date published
December 6, 2021
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 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.
Funder
| Funder name | Awards |
U.S. Department of Energy | JDACS4C |
European Commission | |
Coronavirus CARES Act | |
U.S. Department of Energy | |
DOE | |
Office of Science | |
National Cancer Institute (NCI) of the National Institutes of Health | |
United States Department of Energy | |
National Nuclear Security Administration | |
CSGF | DE-SC0019323 |
Texas Advanced Computing Center | |
EU | 823712 |
UCL | |
MRC | MR/L016311/1 |
Journal title
Interface Focus
Volume
11
Issue
6
Publisher
The Royal Society
eISSN
2042-8901
Official URL
Rights statement
In Copyright