Supercomputing Pipelines Search for Therapeutics Against COVID-19

Vermaas, Josh V.; Sedova, Ada; Baker, Matthew; Boehm, Swen; Rogers, David; Larkin, Jeff; Gläser, Jens; Smith, Micholas Dean; Hernández, Óscar

doi:10.1109/mcse.2020.3036540

Cited by 28 publications

(20 citation statements)

References 15 publications

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“… 56 , 57 The use of GPUs has recently been made possible for the widely used program AutoDock4 58 , 59 resulting in the program AutoDock-GPU, which provides up to 50× speedup over AutoDock4 (available at ). 60 − 64 Thus, the use of leadership HPC facilities for ensemble docking can provide the ability to screen billions of ligands to a full set of conformations generated with HPC-based MD simulations. Quantum mechanical refinement of classical docking ranking based on fragment molecular orbital (FMO) techniques also naturally benefits greatly from massively parallel supercomputer capabilities.…”

Section: Introductionmentioning

confidence: 99%

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

Acharya

Agarwal

Baker

et al. 2020

J. Chem. Inf. Model.

Self Cite

159

141

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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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

Acharya

Agarwal

Baker

et al. 2020

J. Chem. Inf. Model.

Self Cite

159

141

View full text Add to dashboard Cite

show abstract

“…Finally, experiment 5 showed how RP can be effectively and efficiently used to execute hundred of millions of Python function calls on NSF Frontera. In fact, RP enabled approximately 150 × 10 6 docks/hour, about two times the highest known published rate [45].…”

Section: Discussionmentioning

confidence: 98%

“…This workload is a core stage of the DOE NVBL drug discovery pipeline [15] to find known drug molecules that can bind to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Currently, to our knowledge, RP executes docking calculations at the largest scales, and a throughput rate that is twice that of highest published rate [45].…”

Section: A Experiments Designmentioning

confidence: 99%

Design and Performance Characterization of RADICAL-Pilot on Leadership-class Platforms

Merzky¹,

Turilli²,

Титов³

et al. 2021

Preprint

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Many extreme scale scientific applications have workloads comprised of a large number of individual highperformance tasks. The Pilot abstraction decouples workload specification, resource management, and task execution via job placeholders and late-binding. As such, suitable implementations of the Pilot abstraction can support the collective execution of large number of tasks on supercomputers. We introduce RADICAL-Pilot (RP) as a portable, modular and extensible Pilot enabled runtime system. We describe RP's design, architecture and implementation. We characterize its performance and show its ability to scalably execute workloads comprised of tens of thousands heterogeneous tasks on DOE and NSF leadership-class HPC platforms. Specifically, we investigate RP's weak/strong scaling with CPU/GPU, single/multi core, (non)MPI tasks and python functions when using most of ORNL Summit and TACC Frontera. RADICAL-Pilot can be used stand-alone, as well as the runtime for third-party workflow systems.

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“…Although the task schedulers presented here were first put to production use on computational chemistry research with mixed CPU/GPU workloads, [3,4,5] the nonuniformity of those tasks prevent quantitative comparison. This work presents a standard, synthetic benchmark to highlight overheads.…”

Section: Evaluation Methodsmentioning

confidence: 99%

“…The tools presented here have already proven useful on several projects underway on the Summit supercomputer, including managing hundreds of molecular dynamics simulations and analysis steps pmake, [3] running docking and AI-based rescoring (dwork), [4] and summarizing results (mpi-list). [5] This work shows that, when used correctly, they achieve full parallelism with negligible run-time overhead.…”

Section: Introductionmentioning

confidence: 99%

Three Practical Workflow Schedulers for Easy Maximum Parallelism

Rogers¹

2021

Preprint

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Runtime scheduling and workflow systems are an increasingly popular algorithmic component in HPC because they allow full system utilization with relaxed synchronization requirements. There are so many special-purpose tools for task scheduling, one might wonder why more are needed. Use cases seen on the Summit supercomputer needed better integration with MPI and greater flexibility in job launch configurations. Preparation, execution, and analysis of computational chemistry simulations at the scale of tens of thousands of processors revealed three distinct workflow patterns. A separate job scheduler was implemented for each one using extremely simple and robust designs: file-based, task-list based, and bulk-synchronous. Comparing to existing methods shows unique benefits of this work, including simplicity of design, suitability for HPC centers, short startup time, and well-understood per-task overhead. All three new tools have been shown to scale to full utilization of Summit, and have been made publicly available with tests and documentation. This work presents a complete characterization of the minimum effective task granularity for efficient scheduler usage scenarios. These schedulers have the same bottlenecks, and hence similar task granularities as those reported for existing tools following comparable paradigms.

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Supercomputing Pipelines Search for Therapeutics Against COVID-19

Cited by 28 publications

References 15 publications

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

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

Design and Performance Characterization of RADICAL-Pilot on Leadership-class Platforms

Three Practical Workflow Schedulers for Easy Maximum Parallelism

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