FastMDAnalysis: Software for Automated Analysis of Molecular Dynamics Trajectories

The analysis of molecular dynamics (MD) trajectories remains fragmented, requiring researchers to integrate multiple computational methods in bespoke scripts. This creates a significant barrier to reproducibility and limits analytical scope. We present FastMDAnalysis, a unified framework that establishes a reproducible, automated workflow for end-to-end trajectory analysis. The system orchestrates a comprehensive and extensible suite of core analysis modules, including root-mean-square deviation and fluctuation, radius of gyration, hydrogen bonding, solvent-accessible surface area, secondary structure assignment, dimensionality reduction, clustering, fraction of native contacts for protein folding studies, and dihedral angle analysis, within a single, consistent environment built on MDTraj, scikit-learn, and SciPy. The software natively supports all major trajectory formats, including GROMACS, AMBER, and CHARMM. We demonstrate a $>90\%$ reduction in code volume for standard workflows and validate its numerical equivalence to reference implementations. FastMDAnalysis provides a methodological advance that makes rigorous, multi-analysis MD studies accessible and reproducible for the computational chemistry, biology, and biophysics communities. The software is freely available under the MIT license at https://github.com/aai-research-lab/fastmdanalysis.