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Pandemic drugs at pandemic speed: infrastructure for accelerating COVID-19 drug discovery with hybrid machine learning- and physics-based simulations on high-performance computers

Journal Article · · Interface Focus (Online)
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  1. Univ. College London (United Kingdom)
  2. Univ. College London (United Kingdom); Universita di Napoli Frederico II (Italy)
  3. Univ. of Chicago, IL (United States)
  4. RWTH Aachen Univ. (Germany)
  5. Brookhaven National Lab. (BNL), Upton, NY (United States)
  6. Rutgers Univ., Piscataway, NJ (United States)
  7. Brookhaven National Lab. (BNL), Upton, NY (United States); Rutgers Univ., Piscataway, NJ (United States)
  8. Science Museum, London (United Kingdom)
  9. Italian Institute of Technology, Genova (Italy)
  10. Italian Institute of Technology, Rome (Italy)
  11. Bavarian Academy of Sciences and Humanities, Garching bei München (Germany)
  12. European Organization for Nuclear Research (CERN), Geneva (Switzerland)
  13. Argonne National Lab. (ANL), Argonne, IL (United States)
  14. Univ. College London (United Kingdom); Univ. of Amsterdam (Netherlands)
+ Show Author Affiliations
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.
Research Organization:
Brookhaven National Laboratory (BNL), Upton, NY (United States)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA); UK MCR; UK Consortium on Mesoscale Engineering Sciences (UKCOMES); European Commission; Exascale Computing Projects; National Institutes of Health (NIH); European Research Council; USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
Grant/Contract Number:
SC0012704; SC0019323
OSTI ID:
1830193
Alternate ID(s):
OSTI ID: 1841103
OSTI ID: 1902332
Report Number(s):
BNL--222356-2021-JAAM
Journal Information:
Interface Focus (Online), Journal Name: Interface Focus (Online) Journal Issue: 6 Vol. 11; ISSN 2042-8901
Publisher:
The Royal SocietyCopyright Statement
Country of Publication:
United States
Language:
English

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