OpenMM 8: Molecular dynamics simulation with machine learning potentials Journal Article
| Authors: | Eastman, P.; Galvelis, R.; Peláez, R. P.; Abreu, C. R. A.; Farr, S. E.; Gallicchio, E.; Gorenko, A.; Henry, M. M.; Hu, F.; Huang, J.; Krämer, A.; Michel, J.; Mitchell, J. A.; Pande, V. S.; Rodrigues, J. P. G. L. M.; Rodriguez-Guerra, J.; Simmonett, A. C.; Singh, S.; Swails, J.; Turner, P.; Wang, Y.; Zhang, I.; Chodera, J. D.; De Fabritiis, G.; Markland, T. E. |
| Article Title: | OpenMM 8: Molecular dynamics simulation with machine learning potentials |
| Abstract: | Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features in simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations with only a modest increase in cost. © 2023 American Chemical Society. |
| Keywords: | green fluorescent protein; molecular dynamics; water; energy; cyclin-dependent kinase; molecular dynamics simulation; machine learning; chromophores; molecular simulations; machine-learning; dynamics simulation; learning potential; high level interface; potential function |
| Journal Title: | Journal of Physical Chemistry B |
| Volume: | 128 |
| Issue: | 1 |
| ISSN: | 1520-6106 |
| Publisher: | American Chemical Society |
| Date Published: | 2024-01-11 |
| Start Page: | 109 |
| End Page: | 116 |
| Language: | English |
| DOI: | 10.1021/acs.jpcb.3c06662 |
| PUBMED: | 38154096 |
| PROVIDER: | scopus |
| PMCID: | PMC10846090 |
| DOI/URL: | |
| Notes: | Article -- MSK Cancer Center Support Grant (P30 CA008748) acknowledged in PDF -- Source: Scopus |
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