GPU-Accelerated Drug Discovery with Docking on the Summit Supercomputer: Porting, Optimization, and Application to COVID-19 Research

Scott Legrand; Aaron Scheinberg; Andreas F. Tillack; Mathialakan Thavappiragasam; Josh V. Vermaas; Rupesh Agarwal; Jeff Larkin; Duncan Poole; Diogo Santos-Martins; Leonardo Solis-Vasquez; Andreas Koch; Stefano Forli; Oscar Hernandez; Jeremy C. Smith; Ada Sedova

doi:10.1145/3388440.3412472

GPU-Accelerated Drug Discovery with Docking on the Summit Supercomputer: Porting, Optimization, and Application to COVID-19 Research

Scott Legrand, Aaron Scheinberg, Andreas F. Tillack, Mathialakan Thavappiragasam, Josh V. Vermaas, Rupesh Agarwal, Jeff Larkin, Duncan Poole, Diogo Santos-Martins, Leonardo Solis-Vasquez, Andreas Koch, Stefano Forli, Oscar Hernandez, Jeremy C. Smith, Ada Sedova

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

51 Scopus citations

Abstract

Protein-ligand docking is an in silico tool used to screen potential drug compounds for their ability to bind to a given protein receptor within a drug-discovery campaign. Experimental drug screening is expensive and time consuming, and it is desirable to carry out large scale docking calculations in a high-Throughput manner to narrow the experimental search space. Few of the existing computational docking tools were designed with high performance computing in mind. Therefore, optimizations to maximize use of high-performance computational resources available at leadership-class computing facilities enables these facilities to be leveraged for drug discovery. Here we present the porting, optimization, and validation of the AutoDock-GPU program for the Summit supercomputer, and its application to initial compound screening efforts to target proteins of the SARS-CoV-2 virus responsible for the current COVID-19 pandemic.

Original language	English
Title of host publication	Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, BCB 2020
Publisher	Association for Computing Machinery, Inc
ISBN (Electronic)	9781450379649
DOIs	https://doi.org/10.1145/3388440.3412472
State	Published - Sep 21 2020
Event	11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, BCB 2020 - Virtual, Online, United States Duration: Sep 21 2020 → Sep 24 2020

Publication series

Name	Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, BCB 2020

Conference

Conference	11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, BCB 2020
Country/Territory	United States
City	Virtual, Online
Period	09/21/20 → 09/24/20

Funding

This research was sponsored by the Laboratory Directed Research and Development Program at Oak Ridge National Laboratory (ORNL), which is managed by UT-Battelle, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC05-00OR22725, and used resources of the Oak Ridge Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC05-00OR22725. Development of the AutoDock-GPU was supported by the National Institutes of Health (GM069832). We thank Jonathan Lefman and Geetika Gupta (NVIDIA) for essential coordination and communication support.

Funders	Funder number
Office of Science
U.S. Department of Energy
National Institutes of Health	GM069832
Oak Ridge National Laboratory
U.S. Department of Energy	DE-AC05-00OR22725

Keywords

Drug discovery
GPU acceleration
high-performance computing
protein-ligand docking

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1145/3388440.3412472

Cite this

Legrand, S., Scheinberg, A., Tillack, A. F., Thavappiragasam, M., Vermaas, J. V., Agarwal, R., Larkin, J., Poole, D., Santos-Martins, D., Solis-Vasquez, L., Koch, A., Forli, S., Hernandez, O., Smith, J. C., & Sedova, A. (2020). 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, BCB 2020 (Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, BCB 2020). Association for Computing Machinery, Inc. https://doi.org/10.1145/3388440.3412472

Legrand, Scott ; Scheinberg, Aaron ; Tillack, Andreas F. et al. / GPU-Accelerated Drug Discovery with Docking on the Summit Supercomputer : Porting, Optimization, and Application to COVID-19 Research. Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, BCB 2020. Association for Computing Machinery, Inc, 2020. (Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, BCB 2020).

@inproceedings{86d5a4965fc5417798700c71cb0ad4fa,

title = "GPU-Accelerated Drug Discovery with Docking on the Summit Supercomputer: Porting, Optimization, and Application to COVID-19 Research",

abstract = "Protein-ligand docking is an in silico tool used to screen potential drug compounds for their ability to bind to a given protein receptor within a drug-discovery campaign. Experimental drug screening is expensive and time consuming, and it is desirable to carry out large scale docking calculations in a high-Throughput manner to narrow the experimental search space. Few of the existing computational docking tools were designed with high performance computing in mind. Therefore, optimizations to maximize use of high-performance computational resources available at leadership-class computing facilities enables these facilities to be leveraged for drug discovery. Here we present the porting, optimization, and validation of the AutoDock-GPU program for the Summit supercomputer, and its application to initial compound screening efforts to target proteins of the SARS-CoV-2 virus responsible for the current COVID-19 pandemic.",

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author = "Scott Legrand and Aaron Scheinberg and Tillack, {Andreas F.} and Mathialakan Thavappiragasam and Vermaas, {Josh V.} and Rupesh Agarwal and Jeff Larkin and Duncan Poole and Diogo Santos-Martins and Leonardo Solis-Vasquez and Andreas Koch and Stefano Forli and Oscar Hernandez and Smith, {Jeremy C.} and Ada Sedova",

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language = "English",

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Legrand, S, Scheinberg, A, Tillack, AF, Thavappiragasam, M, Vermaas, JV, Agarwal, R, Larkin, J, Poole, D, Santos-Martins, D, Solis-Vasquez, L, Koch, A, Forli, S, Hernandez, O, Smith, JC & Sedova, A 2020, 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, BCB 2020. Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, BCB 2020, Association for Computing Machinery, Inc, 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, BCB 2020, Virtual, Online, United States, 09/21/20. https://doi.org/10.1145/3388440.3412472

GPU-Accelerated Drug Discovery with Docking on the Summit Supercomputer: Porting, Optimization, and Application to COVID-19 Research. / Legrand, Scott; Scheinberg, Aaron; Tillack, Andreas F. et al.
Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, BCB 2020. Association for Computing Machinery, Inc, 2020. (Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, BCB 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Tillack, Andreas F.

AU - Thavappiragasam, Mathialakan

AU - Vermaas, Josh V.

AU - Agarwal, Rupesh

AU - Larkin, Jeff

AU - Poole, Duncan

AU - Santos-Martins, Diogo

AU - Solis-Vasquez, Leonardo

AU - Koch, Andreas

AU - Forli, Stefano

AU - Hernandez, Oscar

AU - Smith, Jeremy C.

AU - Sedova, Ada

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AB - Protein-ligand docking is an in silico tool used to screen potential drug compounds for their ability to bind to a given protein receptor within a drug-discovery campaign. Experimental drug screening is expensive and time consuming, and it is desirable to carry out large scale docking calculations in a high-Throughput manner to narrow the experimental search space. Few of the existing computational docking tools were designed with high performance computing in mind. Therefore, optimizations to maximize use of high-performance computational resources available at leadership-class computing facilities enables these facilities to be leveraged for drug discovery. Here we present the porting, optimization, and validation of the AutoDock-GPU program for the Summit supercomputer, and its application to initial compound screening efforts to target proteins of the SARS-CoV-2 virus responsible for the current COVID-19 pandemic.

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KW - GPU acceleration

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T3 - Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, BCB 2020

BT - Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, BCB 2020

PB - Association for Computing Machinery, Inc

Y2 - 21 September 2020 through 24 September 2020

ER -

Legrand S, Scheinberg A, Tillack AF, Thavappiragasam M, Vermaas JV, Agarwal R 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, BCB 2020. Association for Computing Machinery, Inc. 2020. (Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, BCB 2020). doi: 10.1145/3388440.3412472

GPU-Accelerated Drug Discovery with Docking on the Summit Supercomputer: Porting, Optimization, and Application to COVID-19 Research

Abstract

Publication series

Conference

Funding

Keywords

UN SDGs

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