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Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19

Journal Article · · Journal of Chemical Information and Modeling
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  1. Georgia Institute of Technology, Atlanta, GA (United States)
  2. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Univ. of Tennessee, Knoxville, TN (United States)
  3. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  4. Univ. of Alabama, Huntsville, AL (United States)
  5. Brookhaven National Lab. (BNL), Upton, NY (United States)
  6. Univ. of L'Aquila (Italy)
  7. Univ. of Kentucky, Lexington, KY (United States)
  8. The Scripps Research Inst., La Jolla, CA (United States)
  9. Amazon Web Services, Seattle, WA (United States)
  10. NVIDIA Corp., Santa Clara, CA (United States)
  11. Argonne National Lab. (ANL), Lemont, IL (United States)
  12. Jubilee Development, Cambridge, MA (United States)
  13. City Univ. of New York (CUNY), NY (United States)
  14. CNR Inst. of Nanoscience, Modena (Italy)
+ Show Author Affiliations
In this work, we present a supercomputer-driven pipeline for in silico drug discovery using enhanced sampling molecular dynamics (MD) and ensemble docking. Ensemble docking makes use of MD results by docking compound databases into representative protein binding-site conformations, thus taking into account the dynamic properties of the binding sites. We also describe preliminary results obtained for 24 systems involving eight proteins of the proteome of SARS-CoV-2. The MD involves temperature replica exchange enhanced sampling, making use of massively parallel supercomputing to quickly sample the configurational space of protein drug targets. Using the Summit supercomputer at the Oak Ridge National Laboratory, more than 1 ms of enhanced sampling MD can be generated per day. We have ensemble docked repurposing databases to 10 configurations of each of the 24 SARS-CoV-2 systems using AutoDock Vina. Comparison to experiment demonstrates remarkably high hit rates for the top scoring tranches of compounds identified by our ensemble approach. We also demonstrate that, using Autodock-GPU on Summit, it is possible to perform exhaustive docking of one billion compounds in under 24 h. Finally, we discuss preliminary results and planned improvements to the pipeline, including the use of quantum mechanical (QM), machine learning, and artificial intelligence (AI) methods to cluster MD trajectories and rescore docking poses.
Research Organization:
Argonne National Laboratory (ANL), Argonne, IL (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)
Sponsoring Organization:
USDOE Office of Science (SC); National Institutes of Health (NIH); National Science Foundation (NSF)
DOE Contract Number:
AC02-06CH11357; AC05-00OR22725; AC02-05CH11231
OSTI ID:
1778008
Journal Information:
Journal of Chemical Information and Modeling, Journal Name: Journal of Chemical Information and Modeling Journal Issue: 12 Vol. 60; ISSN 1549-9596
Publisher:
American Chemical Society
Country of Publication:
United States
Language:
English

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Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19
Journal Article · Tue Dec 15 19:00:00 EST 2020 · Journal of Chemical Information and Modeling · OSTI ID:1755144

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