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MDAnalysis is a powerful Python library for analyzing MD simulations. While primarily designed to help you build custom analysis tools, it also supports interactive data exploration in environments like IPython and Jupyter notebooks, especially when combined with pandas. This makes MDAnalysis an excellent choice for rapid prototyping and exploratory analysis.
MDAnalysis is an academic software package, and if you use it in your research, please cite the relevant publications. For details on how to cite MDAnalysis, visit our [Citations]({{ site.baseurl }}/citations/) page.
Whether you're new to MDAnalysis or looking to deepen your expertise, this page will guide you through our learning resources.
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Install MDAnalysis
Follow the instructions in [Getting Started]({{ site.baseurl }}/getting_started/) to install MDAnalysis. -
Quickstart Tutorial
Begin with the [{{ site.docs.quickstart.name }}]({{ site.docs.quickstart.url }}) tutorial to write and run your first MDAnalysis script. -
User Guide
Explore the [{{ site.docs.userguide.name }}]({{ site.docs.userguide.url }}) for detailed tutorials and self-contained examples. -
Tutorials Repository
Browse additional learning resources and code examples in our Tutorials repository. -
Full Documentation
For in-depth technical details, visit the [Documentation]({{ site.baseurl }}/documentation/) page. -
Watch MDAnalysis videos
Learn from conference talks, workshops, and webinars presented by core developers. Explore the Videos section below and our YouTube channel.
If you need help, check out our [Community]({{ site.baseurl }}/community/) page.
The following videos, presented by core developers at conferences, highlight various aspects of MDAnalysis and demonstrate its use in research.
@lilyminium's talk at PyCon AU 2019 The universe as balls and springs: molecular dynamics in Python gives a general introduction to molecular dynamics and shows how to use MDAnalysis (and other tools such as OpenMM, nglviewer, pandas, plotly). If you want to better understand what MD simulations are and how scientists can make use of the vast Python eco-system to analyze (and run) MD simulations, start here:
@orbeckst's talk at SciPy 2016 provides an introduction to the MDAnalysis library, its uses, and underlying philosophy:
Also read the paper MDAnalysis: A Python package for the rapid analysis of molecular dynamics simulations which adds detail to the concepts outlined in this talk.
@jbarnoud presented at the PyGrunn 2017 conference Looking at molecules using Python where he shows how to use a whole range of MDAnalysis from the simple to the advanced in Jupyter notebooks (he also shows off nglview for visualization and datreant for organizing his data):
In this BioExcel webinar, three of the MDAnalysis Core Developers (@orbeckst, @lilyminium, @IAlibay) summarize the basics of MDAnalysis, show more advanced ways to hack MDAnalysis and outline future developments.