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
• Build a protein-ligand system from a PDB identifier alone: the ligand is identified, its chemistry retrieved, and its protonation settled in the binding site, with a refusal rather than a guess where the structure is ambiguous
• Find out what holds a ligand in place, not just how much of the protein it touches: hydrogen bonds, hydrophobic contacts, salt bridges, pi-stacking, pi-cation, halogen bonds, metal coordination and water bridges, each against a published criterion
• Read an occupancy that says how much watching it rests on: a contact present in 450 consecutive frames and one present in 450 alternating frames are both fifty per cent, and only the second has an error bar
• 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
• Analyse a trajectory you already have, from any engine, without running the simulation again
• Build a config in the browser and run it there, or take it to a cluster — the same file either way
• 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

Install

From conda-forge, which brings every backend with it:

conda create -n fastmdxplora -c conda-forge fastmdxplora
conda activate fastmdxplora

This is the recommended route. OpenMM, PDBFixer, OpenFF, RDKit, and PROPKA are conda-forge packages, and the ligand path needs all of them; installing them any other way is more work for the same result.

From PyPI, if you already manage those yourself or only need part of the pipeline:

pip install fastmdxplora            # analysis and reporting
pip install "fastmdxplora[md]"      # adds setup and simulation
pip install "fastmdxplora[ligand]"  # adds most of protein-ligand preparation
pip install "fastmdxplora[pdf]"     # adds the PDF report

Two of those are partial from PyPI, which is why conda-forge is the recommendation rather than a preference.

The ligand path also needs the OpenFF toolkit, which has no PyPI distribution at all — it is a conda-forge package, so no pip command reaches it:

conda install -c conda-forge openff-toolkit openmmforcefields

The pdf extra needs Pango and Cairo present as system libraries. The conda-forge package brings its own; from PyPI they have to be installed separately. Where they are missing the run says so and writes the other formats rather than failing.

Run fastmdx info afterwards to see which backends were found.

Working on FastMDXplora itself, Windows and WSL2, and troubleshooting a partial install are covered 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.

Documentation

Full documentation is at fastmdxplora.readthedocs.io:

• Beginner's guide: first run, start to finish
• Protein-ligand interactions: what holds a ligand, and how confidently it is known
• 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.