research-article

Public Access

Scalable HPC & AI infrastructure for COVID-19 therapeutics

Authors:

Matteo Turilli,

Arvind Ramanathan,

Hubertus Van Dam,

Sean Wilkinson,

Shantenu JhaAuthors Info & Claims

PASC '21: Proceedings of the Platform for Advanced Scientific Computing Conference

Article No.: 2, Pages 1 - 13

https://doi.org/10.1145/3468267.3470573

Published: 26 August 2021 Publication History

Abstract

COVID-19 has claimed more than 2.7 × 10⁶ lives and resulted in over 124 × 10⁶ infections. There is an urgent need to identify drugs that can inhibit SARS-CoV-2. We discuss innovations in computational infrastructure and methods that are accelerating and advancing drug design. Specifically, we describe several methods that integrate artificial intelligence and simulation-based approaches, and the design of computational infrastructure to support these methods at scale. We discuss their implementation, characterize their performance, and highlight science advances that these capabilities have enabled.

References

[1]

Regine S Bohacek, Colin McMartin, and Wayne C Guida. "The art and practice of structure-based drug design: A molecular modeling perspective". In: Medicinal research reviews 16.1 (1996), pp. 3--50.

[2]

Jiankun Lyu et al. "Ultra-large library docking for discovering new chemotypes". In: Nature 566.7743 (2019), pp. 224--229.

[3]

Xiwen Jia et al. "Anthropogenic biases in chemical reaction data hinder exploratory inorganic synthesis". In: Nature 573.7773 (2019), pp. 251--255.

[4]

Matteo Turilli et al. "Middleware building blocks for workflow systems". In: Computing in Science & Engineering 21.4 (2019), pp. 62--75.

[5]

Jens Glaser, Josh V. Vermaas, David M. Rogers, et al. "High-Throughput Virtual Laboratory for Drug Discovery Using Massive Datasets". In: International Journal of High-Performance Computing Applications (to appear) (2020).

[6]

Shunzhou Wan et al. "Rapid and Reliable Binding Affinity Prediction of Bromodomain Inhibitors: A Computational Study". In: Journal of Chemical Theory and Computation 13.2 (2017), pp. 784--795.

[7]

Agastya P. Bhati et al. "Rapid, Accurate, Precise, and Reliable Relative Free Energy Prediction Using Ensemble Based Thermodynamic Integration". In: Journal of Chemical Theory and Computation 13.1 (2017). 27997169, pp. 210--222. eprint: https://doi.org/10.1021/acs.jctc.6b00979. URL: https://doi.org/10.1021/acs.jctc.6b00979.

[8]

"OpenEye Toolkits 2019.Oct.2". Version 2019.Oct.2. In: Open Eye Scientific (2019). URL: http://www.eyesopen.com/.

[9]

Mark R Mcgann et al. "Gaussian docking functions". In: Biopolymers: Original Research on Biomolecules 68.1 (2003), pp. 76--90.

[10]

Yadu Babuji et al. "Targeting SARS-CoV-2 with AI-and HPC-enabled lead generation: A First Data release". In: arXiv preprint arXiv:2006.02431 (2020).

[11]

Gia G. Maisuradze, Adam Liwo, and Harold A. Scheraga. "Principal Component Analysis for Protein Folding Dynamics". In: Journal of Molecular Biology 385.1 (2009), pp. 312--329. ISSN: 0022-2836. URL: http://www.sciencedirect.com/science/article/pii/S0022283608012886.

[12]

Debsindhu Bhowmik et al. "Deep clustering of protein folding simulations". In: BMC Bioinformatics 19.18 (2018), p. 484. URL: https://doi.org/10.1186/s12859-018-2507-5.

[13]

Hyungro Lee et al. "DeepDriveMD: Deep-Learning Driven Adaptive Molecular Simulations for Protein Folding". In: 2019 IEEE/ACM Third Workshop on Deep Learning on Supercomputers (DLS). IEEE. 2019, pp. 12--19. eprint: 1909.07817.

[14]

T. P. Straatsma, H. J. C. Berendsen, and J. P. M. Postma. "Free energy of hydrophobic hydration: A molecular dynamics study of noble gases in water". In: The Journal of Chemical Physics 85.11 (1986), pp. 6720--6727. eprint: https://doi.org/10.1063/1.451846. URL: https://doi.org/10.1063/1.451846.

[15]

Andre Merzky, Ole Weidner, and Shantenu Jha. "SAGA: A standardized access layer to heterogeneous distributed computing infrastructure". In: SoftwareX 1 (2015), pp. 3--8.

[16]

Andre Merzky et al. "Using pilot systems to execute many task workloads on supercomputers". In: Workshop on Job Scheduling Strategies for Parallel Processing. Springer. 2018, pp. 61--82.

[17]

Andre Merzky et al. "Design and Performance Characterization of RADICAL-Pilot on Leadership-class Platforms". In: arXiv preprint arXiv:2103.00091 (2021).

[18]

Vivek Balasubramanian et al. "Harnessing the power of many: Extensible toolkit for scalable ensemble applications". In: 2018 IEEE International Parallel and Distributed Processing Symposium (IPDPS). IEEE. 2018, pp. 536--545.

[19]

Matteo Turilli, Mark Santcroos, and Shantenu Jha. "A comprehensive perspective on pilot-job systems". In: ACM Computing Surveys (CSUR) 51.2 (2018), pp. 1--32.

Digital Library

[20]

Dino Quintero et al. IBM High-Performance Computing Insights with IBM Power System AC922 Clustered Solution. IBM Redbooks, 2019.

[21]

Ralph H. Castain et al. "PMIx: Process management for exascale environments". In: Parallel Computing 79 (2018), pp. 9 --29. ISSN: 0167-8191. URL: http://www.sciencedirect.com/science/article/pii/S0167819118302424.

[22]

Matteo Turilli et al. "Characterizing the Performance of Executing Many-tasks on Summit". In: IPDRM Workshop, SC19 (2019). https://arxiv.org/abs/1909.03057.

[23]

Dong H Ahn et al. "Flux: a next-generation resource management framework for large HPC centers". In: 2014 43rd International Conference on Parallel Processing Workshops. IEEE. 2014, pp. 9--17.

[24]

Oak Ridge Leadership Computing Facility. Summit User Guide. URL: https://docs.olcf.ornl.gov/systems/summit_user_guide.html.

[25]

Aymen Al Saadi et al. "IMPECCABLE: Integrated Modeling PipelinE for COVID Cure by Assessing Better LEads". In: arXiv preprint arXiv:2010.06574 (2020). https://arxiv.org/abs/2010.06574.

[26]

Austin Clyde, Xiaotian Duan, and Rick Stevens. "Regression enrichment surfaces: a simple analysis technique for virtual drug screening models". In: arXiv preprint arXiv:2006.01171 (2020).

[27]

Zhirui Liao et al. "DeepDock: Enhancing Ligand-protein Interaction Prediction by a Combination of Ligand and Structure Information". In: 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE. 2019, pp. 311--317.

[28]

Francesco Gentile et al. "Deep Docking: A Deep Learning Platform for Augmentation of Structure Based Drug Discovery". In: ACS Central Science (2020).

[29]

Christoph Gorgulla et al. "An open-source drug discovery platform enables ultra-large virtual screens". In: Nature 580.7805 (2020), pp. 663--668.

[30]

Josh Vincent Vermaas et al. "Supercomputing Pipelines Search for Therapeutics Against COVID-19". In: Computing in Science & Engineering (2020).

[31]

W Patrick Walters and Renxiao Wang. New Trends in Virtual Screening. 2020.

[32]

Derek Jones et al. Improved Protein-ligand Binding Affinity Prediction with Structure-Based Deep Fusion Inference. 2020. arXiv: 2005.07704 [q-bio.BM].

[33]

Amanda J Minnich et al. "AMPL: A Data-Driven Modeling Pipeline for Drug Discovery". In: Journal of Chemical Information and Modeling 60.4 (2020), pp. 1955--1968.

[34]

Richard E Trager et al. "Docking optimization, variance and promiscuity for large-scale drug-like chemical space using high performance computing architectures". In: Drug discovery today 21.10 (2016), pp. 1672--1680.

[35]

Micholas Smith and Jeremy C Smith. "Repurposing therapeutics for COVID-19: supercomputer-based docking to the SARS-CoV-2 viral spike protein and viral spike proteinhuman ACE2 interface". In: (2020).

[36]

Atanu Acharya et al. "Supercomputer-based ensemble docking drug discovery pipeline with application to Covid-19". In: ChemRxiv (2020).

[37]

Scott LeGrand et al. "GPU-Accelerated Drug Discovery with Docking on the Summit Supercomputer: Porting, Optimization, and Application to COVID-19 Research". In: Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. 2020, pp. 1--10.

Cited By

Zaeri N(2024)Artificial intelligence and machine learning responses to COVID-19 related inquiriesJournal of Medical Engineering & Technology10.1080/03091902.2024.232184647:6(301-320)Online publication date: 16-Apr-2024
https://doi.org/10.1080/03091902.2024.2321846
Zaeri N(2023)Drug discovery for COVID-19 and related mutations using artificial intelligenceResearch Journal of Pharmacy and Technology10.52711/0974-360X.2023.00872(5384-5391)Online publication date: 30-Nov-2023
https://doi.org/10.52711/0974-360X.2023.00872
Meyer LSchouler MCaulk RRibes ARaffin BMohror KArnold DBadia R(2023)High Throughput Training of Deep Surrogates from Large Ensemble RunsProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis10.1145/3581784.3607083(1-16)Online publication date: 12-Nov-2023
https://dl.acm.org/doi/10.1145/3581784.3607083
Show More Cited By

Index Terms

Scalable HPC & AI infrastructure for COVID-19 therapeutics
1. Applied computing
  1. Life and medical sciences
    1. Computational biology
2. Computing methodologies
  1. Machine learning
  2. Modeling and simulation
    1. Simulation types and techniques
      1. Massively parallel and high-performance simulations

Recommendations

Novel Generated Peptides for COVID-19 Targets
BCB '20: Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics

With the world in the midst of a global pandemic, it is important to be able to quickly generate new drug-like compounds for drug research purposes. While some successful work has been done [3, 6] there is still much work to be done, especially as ...
Camphor, Artemisinin and Sumac Phytochemicals as inhibitors against COVID-19: Computational approach
Abstract
Covid-19 is an emerging infectious disease caused by coronavirus SARS-CoV-2. Due to the rapid rise in deaths resulted from this infection all around the world, the identification of drugs against this new coronavirus is an important ...
Highlights
- Hinokiflavone and Myricetin are the structures with best affinity and stability in the binding site of the studied enzyme.
Dual targeting of cytokine storm and viral replication in COVID-19 by plant-derived steroidal pregnanes: An in silico perspective
Abstract
The high morbidity and mortality rate of Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) infection arises majorly from the Acute Respiratory Distress Syndrome and “cytokine storm” syndrome, which is sustained by an ...
Graphical abstract

Display Omitted
Highlights
- The high morbidity and mortality rate from COVID-19 arises from acute respiratory distress and “cytokine storm” syndrome.

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

PASC '21: Proceedings of the Platform for Advanced Scientific Computing Conference

July 2021

186 pages

ISBN:9781450385633

DOI:10.1145/3468267

Conference Chair:
Timothy Robinson
ETH Zurich / CSCS

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGHPC: ACM Special Interest Group on High Performance Computing, Special Interest Group on High Performance Computing

In-Cooperation

CSCS: Swiss National Supercomputing Centre

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 August 2021

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

UK MRC Medical Bioinformatics project
United States Department of Energy
CANDLE project by the ECP
UCL Provost
DOE Office of Science through National Virtual Biotechnology Laboratory
UKCOMES
EU H2020 CompBioMed2 Centre of Excellence

Conference

PASC '21

Sponsor:

SIGHPC

PASC '21: Platform for Advanced Scientific Computing Conference

July 5 - 9, 2021

Geneva, Switzerland

Acceptance Rates

PASC '21 Paper Acceptance Rate 17 of 33 submissions, 52%;

Overall Acceptance Rate 109 of 221 submissions, 49%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

14
Total Citations
View Citations
553
Total Downloads

Downloads (Last 12 months)163
Downloads (Last 6 weeks)22

Reflects downloads up to 06 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Zaeri N(2024)Artificial intelligence and machine learning responses to COVID-19 related inquiriesJournal of Medical Engineering & Technology10.1080/03091902.2024.232184647:6(301-320)Online publication date: 16-Apr-2024
https://doi.org/10.1080/03091902.2024.2321846
Zaeri N(2023)Drug discovery for COVID-19 and related mutations using artificial intelligenceResearch Journal of Pharmacy and Technology10.52711/0974-360X.2023.00872(5384-5391)Online publication date: 30-Nov-2023
https://doi.org/10.52711/0974-360X.2023.00872
Meyer LSchouler MCaulk RRibes ARaffin BMohror KArnold DBadia R(2023)High Throughput Training of Deep Surrogates from Large Ensemble RunsProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis10.1145/3581784.3607083(1-16)Online publication date: 12-Nov-2023
https://dl.acm.org/doi/10.1145/3581784.3607083
Elwasif WNaughton TBaker M(2023)Towards a Standard Process Management Infrastructure for Workflows Using PythonParallel and Distributed Computing, Applications and Technologies10.1007/978-3-031-29927-8_40(523-534)Online publication date: 8-Apr-2023
https://doi.org/10.1007/978-3-031-29927-8_40
Pribec IHachinger SHayek MPringle GBrüchle HJamitzky FMathias G(2023)Efficient and Reliable Data Management for Biomedical ApplicationsHigh Performance Computing for Drug Discovery and Biomedicine10.1007/978-1-0716-3449-3_18(383-403)Online publication date: 3-Jul-2023
https://doi.org/10.1007/978-1-0716-3449-3_18
Ma YLi ZChen XDing BLi NLu TZhang BSuo BJin Z(2023)Machine‐learning assisted scheduling optimization and its application in quantum chemical calculationsJournal of Computational Chemistry10.1002/jcc.2707544:12(1174-1188)Online publication date: 17-Jan-2023
https://doi.org/10.1002/jcc.27075
Ramesh STitov MTurilli MJha SMalony A(2022)The Ghost of Performance Reproducibility Past2022 IEEE 18th International Conference on e-Science (e-Science)10.1109/eScience55777.2022.00091(513-518)Online publication date: Oct-2022
https://doi.org/10.1109/eScience55777.2022.00091
Merzky ATurilli MJha S(2022)RAPTOR: Ravenous Throughput Computing2022 22nd IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid)10.1109/CCGrid54584.2022.00069(595-604)Online publication date: May-2022
https://doi.org/10.1109/CCGrid54584.2022.00069
Jiang ZLuo CGao WWang LZhan J(2022)HPC AI500 V3.0: A scalable HPC AI benchmarking frameworkBenchCouncil Transactions on Benchmarks, Standards and Evaluations10.1016/j.tbench.2022.1000832:4(100083)Online publication date: Oct-2022
https://doi.org/10.1016/j.tbench.2022.100083
Al-Saadi AAhn DBabuji YChard KCorbett JHategan MHerbein SJha SLaney DMerzky AMunson TSalim MTitov MTurilli MUram TWozniak J(2021)ExaWorks: Workflows for Exascale2021 IEEE Workshop on Workflows in Support of Large-Scale Science (WORKS)10.1109/WORKS54523.2021.00012(50-57)Online publication date: Nov-2021
https://doi.org/10.1109/WORKS54523.2021.00012
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Table of Contents