ArticleJournal of chemical information and modeling2022
GROMACS in the Cloud: A Global Supercomputer to Speed Up Alchemical Drug Design.
Article in Journal of chemical information and modeling, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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Who cites it
44 citing papers in PubMed.
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- Genomic structural equation modeling reveals shared genetic structure of cardiac function and structure-function association studies of CLCNKA mutations.Scientific reports · 2026Article
- Rational design and in silico characterization of a multiepitope mRNA vaccine candidate against human metapneumovirus (hMPV) using reverse vaccinology and immunoinformatics approaches.Scientific reports · 2025Article
- In silico-driven protocol for hit-to-lead optimization: a case study on PDE9A inhibitors.Journal of computer-aided molecular design · 2025Article
- Cationic amino acid transporters (CAT) enhance accumulation and susceptibility to the systemic herbicide L-phosphinothricin.Nature communications · 2025Article
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- Identification of potential antigenic proteins and epitopes for the development of a monkeypox virus vaccine: an in silico approach.Molecular diversity · 2025Article
- F-DATA: A Fugaku Workload Dataset for Job-centric Predictive Modelling in HPC Systems.Scientific data · 2025Article
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- Automated On-the-Fly Optimization of Resource Allocation for Efficient Free Energy Simulations.Journal of chemical information and modeling · 2025Article
- Scaling of the GROMACS Molecular Dynamics Code to 65k CPU Cores on an HPC Cluster.Journal of computational chemistry · 2025Article
- Development of a novel multi-epitope mRNA vaccine candidate to combat SFTSV pandemic.PLoS neglected tropical diseases · 2025Article
- Development of a novel multi-epitope subunit mRNA vaccine candidate to combat Acinetobacter baumannii.Scientific reports · 2025Article
- NRGSuite-Qt: a PyMOL plugin for high-throughput virtual screening, molecular docking, normal-mode analysis, the study of molecular interactions, and the detection of binding-site similarities.Bioinformatics advances · 2025Article
- Searching for novel MDM2/MDMX dual inhibitors through a drug repurposing approach.Journal of enzyme inhibition and medicinal chemistry · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We assess costs and efficiency of state-of-the-art high-performance cloud computing and compare the results to traditional on-premises compute clusters. Our use case is atomistic simulations carried out with the GROMACS molecular dynamics (MD) toolkit with a particular focus on alchemical protein-ligand binding free energy calculations. We set up a compute cluster in the Amazon Web Services (AWS) cloud that incorporates various different instances with Intel, AMD, and ARM CPUs, some with GPU acceleration. Using representative biomolecular simulation systems, we benchmark how GROMACS performs on individual instances and across multiple instances. Thereby we assess which instances deliver the highest performance and which are the most cost-efficient ones for our use case. We find that, in terms of total costs, including hardware, personnel, room, energy, and cooling, producing MD trajectories in the cloud can be about as cost-efficient as an on-premises cluster given that optimal cloud instances are chosen. Further, we find that high-throughput ligand-screening can be accelerated dramatically by using global cloud resources. For a ligand screening study consisting of 19 872 independent simulations or ∼200 μs of combined simulation trajectory, we made use of diverse hardware available in the cloud at the time of the study. The computations scaled-up to reach peak performance using more than 4 000 instances, 140 000 cores, and 3 000 GPUs simultaneously. Our simulation ensemble finished in about 2 days in the cloud, while weeks would be required to complete the task on a typical on-premises cluster consisting of several hundred nodes.
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