OpenMM
| OpenMM | |
|---|---|
| Original author | Peter Eastman |
| Developers | Stanford University Memorial Sloan Kettering Cancer Center Pompeu Fabra University National Heart, Lung, and Blood Institute |
| Initial release | January 20, 2010; 15 years ago (January 20, 2010)[1] |
| Stable release | |
| Repository | |
| Written in | C++, C, CUDA, Python |
| Operating system | Linux, macOS, Windows |
| Platform | Many |
| Available in | English |
| Type | Molecular dynamics |
| License | MIT License LGPL |
| Website | openmm |
OpenMM is a library for performing molecular dynamics simulations on a wide variety of hardware architectures. First released in January 2010,[1] it was written by Peter Eastman at the Vijay S. Pande lab at Stanford University. It is notable for its implementation in the Folding@home project's core22 kernel. Core22, also developed at the Pande lab, uses OpenMM to perform protein dynamics simulations on GPUs via CUDA and OpenCL. During the COVID-19 pandemic, a peak of 280,000 GPUs were estimated to be running OpenMM via core22.[3]
Features
[edit ]OpenMM has a C++ API as well as a Python wrapper. Developers are able to customize force fields as well as integrators for low-level simulation control. Users who only require high-level control of their simulations can use built-in force fields (consisting of many commonly used force fields) and built in integrators like Langevin, Verlet, Nosé–Hoover, and Brownian.
See also
[edit ]References
[edit ]- ^ a b "SimTK: OpenMM: Downloads". SimTK. 2020年12月10日. Retrieved 2022年09月09日.
- ^ "Release OpenMM 8.2.0 · openmm/openmm". GitHub. 2024年11月08日. Retrieved 2025年02月27日.
- ^ Zimmerman, Maxwell I.; Porter, Justin R.; Ward, Michael D.; Singh, Sukrit; Vithani, Neha; Meller, Artur; Mallimadugula, Upasana L.; Kuhn, Catherine E.; Borowsky, Jonathan H.; Wiewiora, Rafal P.; Hurley, Matthew F. D.; Harbison, Aoife M.; Fogarty, Carl A.; Coffland, Joseph E.; Fadda, Elisa; Voelz, Vincent A.; Chodera, John D.; Bowman, Gregory R. (2021年05月24日). "SARS-CoV-2 simulations go exascale to predict dramatic spike opening and cryptic pockets across the proteome". Nature Chemistry. 13 (7). Springer Science and Business Media LLC: 651–659. Bibcode:2021NatCh..13..651Z. doi:10.1038/s41557-021-00707-0 . ISSN 1755-4330. PMC 8249329 . PMID 34031561.