OpenMM 7: Rapid development of high performance algorithms for molecular dynamics

• Published in: PLoS computational biology Vol. 13; no. 7; p. e1005659
• Main Authors: Eastman, Peter, Swails, Jason, Chodera, John D., McGibbon, Robert T., Zhao, Yutong, Beauchamp, Kyle A., Wang, Lee-Ping, Simmonett, Andrew C., Harrigan, Matthew P., Stern, Chaya D., Wiewiora, Rafal P., Brooks, Bernard R., Pande, Vijay S.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.07.2017; Public Library of Science (PLoS)
• Subjects: Algorithms; Biology; Biology and Life Sciences; Cancer; Chemistry; Computational Biology - methods; Computer and Information Sciences; Computer simulation; Engineering and Technology; Integration; Methods; Molecular biology; Molecular dynamics; Molecular Dynamics Simulation; Physical Sciences; R&D; Research & development; Research and Analysis Methods; Scholarships & fellowships; Simulation; Software; Technology application; United States > US; Maryland; California
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1005659

OpenMM is a molecular dynamics simulation toolkit with a unique focus on extensibility. It allows users to easily add new features, including forces with novel functional forms, new integration algorithms, and new simulation protocols. Those features automatically work on all supported hardware types (including both CPUs and GPUs) and perform well on all of them. In many cases they require minimal coding, just a mathematical description of the desired function. They also require no modification to OpenMM itself and can be distributed independently of OpenMM. This makes it an ideal tool for researchers developing new simulation methods, and also allows those new methods to be immediately available to the larger community.