GPU-Accelerated Drug Discovery with Docking on the Summit Supercomputer

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

doi:10.1145/3388440.3412472

Cited by 47 publications

(28 citation statements)

References 36 publications

(46 reference statements)

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“…This experience revealed some computational limitations of current small-molecule docking techniques: with I/O bottlenecks, high variability in time-tosolution for docking different molecules, a lack of optimization for the high-throughput docking problem, and overall, performance that did not harness the true power of the Summit supercomputer which rests mainly in its 27,000+ GPUs. We therefore addressed these problems with several HPC-focused modifications to accelerate docking [6].…”

Section: Accelerating Molecular Dockingmentioning

confidence: 99%

“…In the older CPU docking codes, where docking of one compound could take minutes to calculate with significant variability, the time spent reading small input files is insignificant. However on the GPU where FLOPs are abundant, a high-throughput version of the application became limited by I/O and set-up time, as the docking calculations themselves only took a few seconds at most [6]. If the docking is performed on the GPU, using idle CPUs to prefetch data for the next calculation can yield a substantial improvement in performance.…”

Section: Gpu Acceleration and Addressing I/o For The High-throughput Use Casementioning

confidence: 99%

“…A set of 9 distinct viral proteins formed the basis for our structural studies combining molecular simulation, small molecule docking, and analysis to provide a set of compounds predicted to bind to SARS-CoV-2 targets for experimental validation [5]. Emerging results from our recent efforts using high-performance computing (HPC) on the Summit supercomputer, housed at the Oak Ridge Leadership Computing Facility (OLCF), to discover potential COVID-19 therapeutics highlight the impact of GPU acceleration and massive parallelism to substantially reduce computational time to solution as part of a larger drug discovery pipeline [5,6].…”

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confidence: 99%

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Supercomputing Pipelines Search for Therapeutics Against COVID-19

et al. 2021

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The urgent search for drugs to combat SARS-CoV-2 has included the use of supercomputers. The use of general-purpose graphical processing units (GPUs), massive parallelism, and new software for high performance computing (HPC) has allowed researchers to search the vast chemical space of potential drugs faster than ever before. We developed a new drug discovery pipeline using the Summit supercomputer at Oak Ridge National Laboratory to help pioneer this effort, with new platforms that incorporate GPU-accelerated simulation and allow for the virtual screening of billions of potential drug compounds in days compared to weeks or months for their ability to inhibit SARS-COV-2 proteins. This effort will accelerate the process of developing drugs to combat the current COVID-19 pandemic and other diseases.

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Section: Accelerating Molecular Dockingmentioning

confidence: 99%

Section: Gpu Acceleration and Addressing I/o For The High-throughput Use Casementioning

confidence: 99%

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confidence: 99%

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Supercomputing Pipelines Search for Therapeutics Against COVID-19

et al. 2021

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“…Recent work targeting COVID-19 at the Oak Ridge Leadership Computing Facility (OLCF) used the GPU-accelerated particle-grid-based program AutoDock-GPU [6], [9], [10] to screen billions of compounds against the SARS-CoV-2 Main Protease on the Summit supercomputer in a few days, an unprecedented achievement [7]. This program was recently optimized to maximize GPU usage and efficiency in the high-throughput setting, especially for deployment on Summit [6]. The ability to rapidly search the vast chemical space computationally, to filter out potential high-affinity binders against a protein target can aid experimental efforts, which can never screen even a tiny fraction of these molecules at the bench-top.…”

Section: A Autodock-gpumentioning

confidence: 99%

“…Virtual compound screening for drug discovery using molecular docking programs has been a major computational focus of a lot of pharmaceutical efforts. As part of our contribution we have been involved in the development of optimizations to molecular docking programs that use GPU acceleration for deployment on leadership computing facilities to perform massive virtual screens for potential therapeutics [6]- [8].…”

Section: Introductionmentioning

confidence: 99%

Performance Portability of Molecular Docking Miniapp On Leadership Computing Platforms

Thavappiragasam

Scheinberg²,

Elwasif

et al. 2020

2020 IEEE/ACM International Workshop on Performance, Portability and Productivity in HPC (P3HPC)

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Rapidly changing computer architectures, such as those found at high-performance computing (HPC) facilities, present the need for mini-applications (miniapps) that capture essential algorithms used in large applications to test program performance and portability, aiding transitions to new systems. The COVID-19 pandemic has fueled a flurry of activity in computational drug discovery, including the use of supercomputers and GPU acceleration for massive virtual screens for therapeutics. Recent work targeting COVID-19 at the Oak Ridge Leadership Computing Facility (OLCF) used the GPU-accelerated program AutoDock-GPU to screen billions of compounds on the Summit supercomputer. In this paper we present the development of a new miniapp, miniAutoDock-GPU, that can be used to evaluate the performance and portability of GPU-accelerated proteinligand docking programs on different computer architectures. These tests are especially relevant as facilities transition from petascale systems and prepare for upcoming exascale systems that will use a variety of GPU vendors. The key calculations, namely, the Lamarckian genetic algorithm combined with a local search using a Solis-Wets based random optimization algorithm, are implemented. We developed versions of the miniapp using several different programming models for GPU acceleration, including a version using the CUDA runtime API for NVIDIA GPUs, and the Kokkos middle-ware API which is facilitated by C++ template libraries. A third version, currently in progress, uses the HIP programming model. These efforts will help facilitate the transition to exascale systems for this important emerging HPC application, as well as its use on a wide range of heterogeneous platforms.Index Terms-heterogeneous system, high-performance computing, performance portability, hybrid parallel programming model, molecular docking, drug discovery.

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