FastMDXplora
Fully Automated SysTem for Molecular Dynamics eXploration
FastMDXplora explores a protein's behavior end to end from a single command. Given a structure (or just a PDB ID) it performs molecular dynamics exploration all the way through setup, simulation, analysis, and reporting, then hands back publication-ready results.
setup → simulation → analysis → report
Highlights
- Explore a protein's full dynamics with a single command, covering setup, simulation, analysis, and reporting
- Probe protein-ligand binding automatically with analyses for pose stability, contacts, and protein-ligand hydrogen bonds
- Reach beyond plain MD with built-in PLUMED enhanced sampling (metadynamics, umbrella sampling, steered MD)
- Design, start, watch, and review an exploration from a browser, with a 3D viewer and live telemetry
- Scale from a quick single-protein exploration to large parallel campaigns, driven the same way from the CLI or the Python API
Phases of FastMDXplora
| Phase | What it does |
|---|---|
| setup | Cleans up your structure and builds a simulation-ready system: fixes missing atoms, adds hydrogens, solvates, and adds ions. |
| simulation | Runs the molecular dynamics (energy minimization, equilibration, and production), with optional enhanced sampling. |
| analysis | Computes the standard structural and dynamic metrics (and protein-ligand metrics when a ligand is present), with figures ready to use. |
| report | Packages everything into a slide deck, a written report, and a self-contained bundle you can share. |
Install
Analysis and reporting are pure pip. Setup and simulation additionally need OpenMM and PDBFixer, which come from conda-forge.
# analysis and reporting only
pip install fastmdxplora
# all four phases
conda create -n fastmdxplora "python>=3.9,<3.14"
conda activate fastmdxplora
conda install -c conda-forge openmm pdbfixer openmmforcefields
pip install fastmdxplora
To work on FastMDXplora itself, clone the repository and use the bundled
environment.yml. Full instructions, including Windows and WSL2, optional
extras, and troubleshooting, are in the
installation guide.
Quick start
fastmdx explore --system 1L2Y # a full exploration, from a PDB ID
fastmdx gui # design, start, and watch one in a browser
fastmdx info # what is installed and which backends are present
From Python:
import fastmdxplora as fastmdx
runs = fastmdx.FastMDXplora(system="1L2Y", output_dir="trpcage").explore()
print(runs[0].output_dir)
For anything beyond a quick run, capture the whole exploration in a YAML file.
fastmdx init-config writes a commented template, and the same file drives
both the CLI and the Python API:
fastmdx explore --config study.yml
Parameter sweeps, multi-system campaigns, cross-run comparison, parallel execution, and the full flag list are covered in the usage examples and the CLI reference.
Outputs by phase
Each phase writes to its own subdirectory under the output root, with a parameters manifest so every artifact is traceable to the options that produced it.
| Phase | Key outputs |
|---|---|
setup |
prepared.pdb, solvated.pdb, setup_parameters.json |
simulation |
production.dcd, topology.pdb, simulation_parameters.json |
analysis |
<analysis>/*.dat, <analysis>/*.png, analysis_manifest.json |
report |
report.md, dashboard.html, slides.pptx, project_bundle.zip |
Documentation
Full documentation is at fastmdxplora.readthedocs.io:
- Beginner's guide: first run, start to finish
- Installation: every route, platform notes, troubleshooting
- GUI: the browser interface, and watching a cluster run
- Configuration: the YAML file, option by option
- CLI reference: every command and flag
- Production runs: GPUs, long trajectories, scaling
Citation
If you use FastMDXplora in your work, please cite:
Aina, A.; Kwan, D. FastMDAnalysis: Software for Automated Analysis of Molecular Dynamics Trajectories. J. Comput. Chem. 2026, 47, e70350. DOI: 10.1002/jcc.70350
@article{aina2026fastmd,
author = {Aina, Adekunle and Kwan, Derrick},
title = {FastMDAnalysis: Software for Automated Analysis of Molecular Dynamics Trajectories},
journal = {Journal of Computational Chemistry},
volume = {47},
number = {8},
pages = {e70350},
year = {2026},
doi = {10.1002/jcc.70350},
}
Contributing
Contributions are welcome. See CONTRIBUTING.md. FastMDXplora follows the Contributor Covenant.
License
MIT. See LICENSE.
Acknowledgements
FastMDXplora is developed in the AAI Research Lab at California State University Dominguez Hills. It builds on a deep ecosystem of open-source scientific Python: MDTraj, OpenMM, PDBFixer, NumPy, SciPy, scikit-learn, Matplotlib, python-pptx, and many others.
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