The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials
Open accessAbstract
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.
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Cites
OpenMM 7: Rapid development of high performance algorithms for molecular dynamics
Eastman, P., Swails, J., Chodera, J. D., McGibbon, R. T., Zhao, Y., Beauchamp, K. A., Wang, L.-P., Simmonett, A. C., Harrigan, M. P., Stern, C. D., et al. (2017) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005659 · Cited by 2,748
Self-citationANI-1: an extensible neural network potential with DFT accuracy at force field computational cost
Smith, J. S., Isayev, O., & Roitberg, A. E. (2017) · Chemical Science · doi:10.1039/c6sc05720a · Cited by 1,921
Najibi, A., & Goerigk, L. (2018) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.8b00842 · Cited by 320
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“Briefly, 3D molecular dynamics simulations of a chromosome with cohesin (loop extruders), CTCF sites (loop extruder stall sites) and affinity-based looping were performed using the Polychrom package (v.0.0.1) 94 , which wraps the OpenMM molecular simulation toolkit 95 .”
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Predicting the structures of cyclic peptides containing unnatural amino acids by HighFold2
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Metadata
- 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., 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.
- Venue
- The Journal of Physical Chemistry B
- Publisher
- American Chemical Society (ACS)
- Published
- 2023-12-28
- Volume
- 128
- Issue
- 1
- Pages
- 109-116
- ISSN
- 1520-5207 (ISSN-L)
- 1520-6106
- OpenAlex
- W4388870365
- Cited by
- 560
- References
- 3
- FWCI
- 39.40
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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