Journal
INTERFACE FOCUS
Volume 11, Issue 6, Pages -Publisher
ROYAL SOC
DOI: 10.1098/rsfs.2021.0018
Keywords
machine learning; artificial intelligence; novel drug design; molecular dynamics; free energy predictions
Categories
Funding
- UK MRC Medical Bioinformatics project [MR/L016311/1]
- UK Consortium on Mesoscale Engineering Sciences (UKCOMES) [EP/L00030X/1]
- European Commission for the EU [823712]
- EU [800957]
- UCL Provost
- Department of Energy (DOE) Office of Science through the National Virtual Biotechnology Laboratory
- DOE national laboratories focused on response to COVID-19
- Exascale Computing Project [17-SC-20-SC]
- U.S. Department of Energy Office of Science
- National Nuclear Security Administration
- Joint Design of Advanced Computing Solutions for Cancer (JDACS4C) programme by the U.S. DOE
- National Cancer Institute (NCI) of the National Institutes of Health
- United States Department of Energy through the Computational Sciences Graduate Fellowship (DOE CSGF) [DE-SC0019323]
- European Research Council [739964]
- Coronavirus CARES Act
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The existing drug discovery process is costly, inefficient, and slow, but new opportunities lie at the interface between machine learning and physics-based methods. An innovative infrastructure development combines these methods to accelerate drug discovery, relying on supercomputing for high throughput. This approach has demonstrated viability for identifying lead antiviral compounds through repurposing on various supercomputers.
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.
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