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Acceleration of the GROMACS Free-Energy Perturbation Calculations on GPUs

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Název: Acceleration of the GROMACS Free-Energy Perturbation Calculations on GPUs
Autoři: Yiqi Chen, Jian Yang
Rok vydání: 2025
Sbírka: The University of Auckland: Figshare
Témata: Biophysics, Biochemistry, Pharmacology, Cancer, Biological Sciences not elsewhere classified, Chemical Sciences not elsewhere classified, Information Systems not elsewhere classified, long computation times, ligand binding affinities, https :// github, drug discovery pipelines, metax c500 gpus, core cpu implementation, computationally efficient solution, complex workflow setups, around 48 h, enabled fep calculations, conducting fep calculations, energy perturbation calculations, energy perturbation, energy calculations, previous cpu, gpus free, around 1, energy community, widespread adoption, thereby paving, source project, promising tool, nvidia a100
Popis: Free-energy perturbation (FEP) calculations have emerged as a promising tool for the accurate prediction of ligand binding affinities. However, their widespread adoption in drug discovery pipelines has been hindered by long computation times and complex workflow setups. Here, we introduce an optimized graphics processing unit (GPU)-resident FEP implementation within GROMACS. The GPU-enabled FEP calculations are validated on a benchmark system containing eight ligand–protein pairs, including two charged ligands, on both the Nvidia A100 and the MetaX C500 GPU platforms. The absolute binding free energies predicted on these GPUs show excellent agreement (around 1.0 kcal/mol) with previous CPU-computed results. Compared to a 32-core CPU implementation, the GPU-accelerated FEP calculations demonstrate significant speed-ups, with up to nearly 800 and 400% improvements on Nvidia A100 and MetaX C500 GPUs, respectively. The end-to-end absolute binding free-energy calculations for the benchmark systems are reduced from 400 h to around 48 h on the A100 GPU. These advancements aim to provide the alchemical free-energy community with a fast and efficient way of conducting FEP calculations, thereby paving the way for a highly accurate and computationally efficient solution in predicting ligand–protein binding free energies. All codes, data, and scripts are included in our open-source project, FEP-on-GPU workflow, freely available at https://github.com/yiqichenshallwetalk/FEP-on-GPU-Workflow.
Druh dokumentu: article in journal/newspaper
Jazyk: unknown
DOI: 10.1021/acsomega.5c00151.s001
Dostupnost: https://doi.org/10.1021/acsomega.5c00151.s001
https://figshare.com/articles/journal_contribution/Acceleration_of_the_GROMACS_Free-Energy_Perturbation_Calculations_on_GPUs/29194550
Rights: CC BY-NC 4.0
Přístupové číslo: edsbas.928122C4
Databáze: BASE
Popis
Abstrakt:Free-energy perturbation (FEP) calculations have emerged as a promising tool for the accurate prediction of ligand binding affinities. However, their widespread adoption in drug discovery pipelines has been hindered by long computation times and complex workflow setups. Here, we introduce an optimized graphics processing unit (GPU)-resident FEP implementation within GROMACS. The GPU-enabled FEP calculations are validated on a benchmark system containing eight ligand–protein pairs, including two charged ligands, on both the Nvidia A100 and the MetaX C500 GPU platforms. The absolute binding free energies predicted on these GPUs show excellent agreement (around 1.0 kcal/mol) with previous CPU-computed results. Compared to a 32-core CPU implementation, the GPU-accelerated FEP calculations demonstrate significant speed-ups, with up to nearly 800 and 400% improvements on Nvidia A100 and MetaX C500 GPUs, respectively. The end-to-end absolute binding free-energy calculations for the benchmark systems are reduced from 400 h to around 48 h on the A100 GPU. These advancements aim to provide the alchemical free-energy community with a fast and efficient way of conducting FEP calculations, thereby paving the way for a highly accurate and computationally efficient solution in predicting ligand–protein binding free energies. All codes, data, and scripts are included in our open-source project, FEP-on-GPU workflow, freely available at https://github.com/yiqichenshallwetalk/FEP-on-GPU-Workflow.
DOI:10.1021/acsomega.5c00151.s001