MDAnalysis: A Python Package for the Rapid Analysis of Molecular Dynamics Simulations
By Richard J. Gowers, Max Linke, J. Barnoud, T. Reddy, M. Melo, Sean L. Seyler, J. Domanski, David L. Dotson, Sébastien Buchoux, Ian M. Kenney and 1 more. SciPy, 2016
1,400 citations recorded by the source corpus.
MDAnalysis (http://mdanalysis.org) is a library for structural and temporal analysis of molecular dynamics (MD) simulation trajectories and individual protein structures. MD simulations of biological molecules have become an important tool to elucidate the relationship between molecular structure and physiological function. Simulations are performed with highly optimized software packages on HPC resources but most codes generate output trajectories in their own formats so that the development of new trajectory analysis algorithms is confined to specific user communities and widespread adoption and further development is delayed. MDAnalysis addresses this problem by abstracting access to the raw simulation data and presenting a uniform object-oriented Python interface to the user. It thus enables users to rapidly write code that is portable and immediately usable in virtually all biomolecular simulation communities. The user interface and modular design work equally well in complex scripted work flows, as foundations for other packages, and for interactive and rapid prototyping work in IPython / Jupyter notebooks, especially together with molecular visualization provided by nglview and time series analysis with pandas. MDAnalysis is written in Python and Cython and uses NumPy arrays for easy interoperability with the wider scientific Python ecosystem. It is widely used and forms the foundation for more specialized biomolecular simulation tools. MDAnalysis is available under the GNU General Public License v2.
Subfields: Materials Chemistry, Molecular Biology, Plant Science
View Paper: https://www.semanticscholar.org/paper/7d244bae5b7f68ea00ed0da434aee7a88579b824
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Cédric Bouysset
- S. Fiorucci
Citations in the corpus
- ProLIF: a library to encode molecular interactions as fingerprints (2021, methodology): “ProLIF can deal with RDKit [21] molecules or MDAn-alysis [22] Universe objects as input, which allows supporting most 3D molecular formats, from docking to MD simulations.”