FastMDXplora

Molecular dynamics from a PDB code to a finished study — in one command.

Documentation · Quick start · GUI · Cite

fastmdx explore --system 181L

setup  →  simulation  →  analysis  →  report

Four characters of PDB ID as input. FastMDXplora fetches T4 lysozyme, parameterises the benzene bound in its cavity, runs the dynamics, analyses the trajectory, works out which residues hold the ligand in place, and writes the whole study up as a PDF.

Or configure the whole MD study in the GUI:

fastmdx gui

Install

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

fastmdx info lists every backend and how to get anything missing:

fastmdx info

What you can study


A protein on its own	Fold, flexibility, secondary structure, native contacts, conformational clustering — from a PDB code. Fluctuations can be set against the crystal's own B-factors, and backbone order parameters against what NMR relaxation measures.
A protein with a ligand	The ligand is found, its chemistry resolved, its protonation settled in the binding site. Eight interaction types against published criteria tell you what holds it, not just what it touches.
A membrane protein	Embedded in one of seven bilayers, with the orientation checked rather than assumed and pressure coupling that suits a lipid system.
Free energy along a coordinate	Umbrella sampling, metadynamics and steered MD from a named collective variable — nine of them, one or two at a time — without writing PLUMED input. Each says what its output is and is not: a surface if the bias converged, a pathway and the work along it, a potential of mean force if the windows overlap.
A trajectory from another engine	Skip the simulation and analyse what you already have — GROMACS `.xtc` and `.trr`, Amber `.nc`, NAMD and CHARMM `.dcd`, LAMMPS `.lammpstrj`, or `.pdb`, `.cif` and `.h5` that carry their own topology.
Many systems at once	Mutants against wild type — named as `L99A`, and checked against the residue actually there — a sweep across a setting, runs pinned one per GPU, and a comparison report across all of them. Where the members differ only by seed, the spread of their means is set against the error each run claimed for itself.

It refuses rather than guesses. An ambiguous ligand charge, a protein backwards in its membrane, a free-energy surface that never converged — each stops the run and is named, not papered over. Biased averages are corrected to equilibrium where the bias allows and labelled where it does not. Every step explains itself and cites the paper worth reading. What comes out, you can defend; what you cannot is marked.

The config is the study

A FastMDXplora config is the whole description of a molecular dynamics study: the system, how it is prepared, how it is simulated, what is measured, and how it is written up. Capture that, and the four phases — setup, simulation, analysis, report — run themselves.

systems:
  - system: 181L
simulation:
  duration_ns: 100

That is a complete study. Everything unnamed takes a documented default, and every run writes resolved_config.yml with defaults, file and command line merged, so the exact study can be run again by anyone holding that one file.

Three ways to build a config, and each of them also runs all four phases:


The GUI	`fastmdx gui`. A form generated from the schema, so every system and every setting is reachable. Worth using even for a command-line or Python workflow: build the study where the options are visible and explained, then take the file away.
The CLI	`fastmdx explore --config study.yml`, or `fastmdx init-config` for a commented template, or a flag for any setting.
The Python API	`FastMDXplora(config="study.yml").explore()`, or the same blocks passed as options.

None of these is the primary interface and none is a subset of another — form, flags and API are generated from one declaration — and a study designed in the GUI on a laptop runs unchanged on a cluster, because what travels is the config.

Documentation

Start here — Install · Your first run · The GUI · The four phases

Going further — Restraints, membranes, enhanced sampling · Production and GPUs · Protein-ligand interactions

Reference — CLI · Configuration · Examples · Python API

Citation

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 welcome — see CONTRIBUTING.md. FastMDXplora follows the Contributor Covenant.

License

MIT. See LICENSE.

Built in the AAI Research Lab at California State University Dominguez Hills, on MDTraj, OpenMM, PDBFixer, OpenFF, RDKit, NumPy, SciPy, scikit-learn and Matplotlib.

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