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Large-Scale Docking in the Cloud

[Image: see text] Molecular docking is a pragmatic approach to exploit protein structures for new ligand discovery, but the growing size of available chemical space is increasingly challenging to screen on in-house computer clusters. We have therefore developed AWS-DOCK, a protocol for running UCSF...

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Detalles Bibliográficos
Autores principales: Tingle, Benjamin I., Irwin, John J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2023
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10170500/
https://www.ncbi.nlm.nih.gov/pubmed/37071086
http://dx.doi.org/10.1021/acs.jcim.3c00031
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author Tingle, Benjamin I.
Irwin, John J.
author_facet Tingle, Benjamin I.
Irwin, John J.
author_sort Tingle, Benjamin I.
collection PubMed
description [Image: see text] Molecular docking is a pragmatic approach to exploit protein structures for new ligand discovery, but the growing size of available chemical space is increasingly challenging to screen on in-house computer clusters. We have therefore developed AWS-DOCK, a protocol for running UCSF DOCK in the AWS cloud. Our approach leverages the low cost and scalability of cloud resources combined with a low-molecule-cost docking engine to screen billions of molecules efficiently. We benchmarked our system by screening 50 million HAC 22 molecules against the DRD4 receptor with an average CPU time of around 1 s per molecule. We saw up to 3-fold variations in cost between AWS availability zones. Docking 4.5 billion lead-like molecules, a 7 week calculation on our 1000-core lab cluster, runs in about a week depending on accessible CPUs, in AWS for around $25,000, less than the cost of two new nodes. The cloud docking protocol is described in easy-to-follow steps and may be sufficiently general to be used for other docking programs. All the tools to enable AWS-DOCK are available free to everyone, while DOCK 3.8 is free for academic research.
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spelling pubmed-101705002023-05-11 Large-Scale Docking in the Cloud Tingle, Benjamin I. Irwin, John J. J Chem Inf Model [Image: see text] Molecular docking is a pragmatic approach to exploit protein structures for new ligand discovery, but the growing size of available chemical space is increasingly challenging to screen on in-house computer clusters. We have therefore developed AWS-DOCK, a protocol for running UCSF DOCK in the AWS cloud. Our approach leverages the low cost and scalability of cloud resources combined with a low-molecule-cost docking engine to screen billions of molecules efficiently. We benchmarked our system by screening 50 million HAC 22 molecules against the DRD4 receptor with an average CPU time of around 1 s per molecule. We saw up to 3-fold variations in cost between AWS availability zones. Docking 4.5 billion lead-like molecules, a 7 week calculation on our 1000-core lab cluster, runs in about a week depending on accessible CPUs, in AWS for around $25,000, less than the cost of two new nodes. The cloud docking protocol is described in easy-to-follow steps and may be sufficiently general to be used for other docking programs. All the tools to enable AWS-DOCK are available free to everyone, while DOCK 3.8 is free for academic research. American Chemical Society 2023-04-18 /pmc/articles/PMC10170500/ /pubmed/37071086 http://dx.doi.org/10.1021/acs.jcim.3c00031 Text en © 2023 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by/4.0/Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Tingle, Benjamin I.
Irwin, John J.
Large-Scale Docking in the Cloud
title Large-Scale Docking in the Cloud
title_full Large-Scale Docking in the Cloud
title_fullStr Large-Scale Docking in the Cloud
title_full_unstemmed Large-Scale Docking in the Cloud
title_short Large-Scale Docking in the Cloud
title_sort large-scale docking in the cloud
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10170500/
https://www.ncbi.nlm.nih.gov/pubmed/37071086
http://dx.doi.org/10.1021/acs.jcim.3c00031
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