The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Promoting transparency and reproducibility in enhanced molecular simulations
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Citations
Cites
Fast Parallel Algorithms for Short-Range Molecular Dynamics
Plimpton, S. (1995) · Journal of Computational Physics · doi:10.1006/jcph.1995.1039 · Cited by 44,721
Scalable molecular dynamics with NAMD
Phillips, J. C., Braun, R., Wang, W., Gumbart, J., Tajkhorshid, E., Villa, E., Chipot, C., Skeel, R. D., Kalé, L., & Schulten, K. (2005) · Journal of Computational Chemistry · doi:10.1002/jcc.20289 · Cited by 16,914
The Amber biomolecular simulation programs
Case, D. A., Cheatham, T. E., Darden, T., Gohlke, H., Luo, R., Merz, K. M., Onufriev, A., Simmerling, C., Wang, B., & Woods, R. J. (2005) · Journal of Computational Chemistry · doi:10.1002/jcc.20290 · Cited by 9,535
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
Using collective variables to drive molecular dynamics simulations
Fiorin, G., Klein, M. L., & Hénin, J. (2013) · Molecular Physics · doi:10.1080/00268976.2013.813594 · Cited by 1,053
Cited by
Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems
Keith, J. A., Vassilev-Galindo, V., Cheng, B., Chmiela, S., Gastegger, M., Müller, K.-R., & Tkatchenko, A. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00107 · Cited by 849
Bat coronaviruses related to SARS-CoV-2 and infectious for human cells
Temmam, S., Vongphayloth, K., Baquero, E., Munier, S., Bonomi, M., Regnault, B., Douangboubpha, B., Karami, Y., Chrétien, D., Sanamxay, D., et al. (2022) · Nature · doi:10.1038/s41586-022-04532-4 · Cited by 528
Rethinking Metadynamics: From Bias Potentials to Probability Distributions
Invernizzi, M., & Parrinello, M. (2020) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.0c00497 · Cited by 359
Machine learning approaches for analyzing and enhancing molecular dynamics simulations
Wang, Y., Lamim Ribeiro, J. M., & Tiwary, P. (2020) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2019.12.016 · Cited by 303
Self-citationCHARMM at 45: Enhancements in Accessibility, Functionality, and Speed
Hwang, W., Austin, S. L., Blondel, A., Boittier, E. D., Boresch, S., Buck, M., Buckner, J., Caflisch, A., Chang, H.-T., Cheng, X., et al. (2024) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.4c04100 · Cited by 136
Metadata
- Authors
- The PLUMED consortium
- Venue
- Nature Methods
- Published
- 2019-07-30
- Volume
- 16
- Issue
- 8
- Pages
- 670-673
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- OpenAlex
- W2970740744
- Cited by
- 1,194
- References
- 5
- FWCI
- 119.14
- Type
- journal article
- Language
- en
- Open access
- green
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- The PLUMED consortium. (2019). Promoting transparency and reproducibility in enhanced molecular simulations. Nature Methods. https://doi.org/10.1038/s41592-019-0506-8
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
