A Python library for reading, writing, and analyzing molecular dynamics trajectories with support for numerous file formats.
MDTraj is a Python library for analyzing molecular dynamics trajectories, enabling researchers to read, write, and process simulation data from various file formats. It provides fast computational methods for tasks like RMSD calculations, hydrogen bonding analysis, and structural measurements. The library addresses the need for efficient, interoperable tools in computational chemistry and biophysics.
Computational chemists, biophysicists, and researchers working with molecular dynamics simulations who need to analyze trajectory data programmatically.
Developers choose MDTraj for its extensive file format support, high-performance analysis functions, and lightweight API optimized for speed and ease of use in scientific workflows.
An open library for the analysis of molecular dynamics trajectories
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.
Reads and writes from every major MD format imaginable including PDB, XTC, TRR, DCD, and HDF5, as explicitly listed in the README, ensuring interoperability with diverse simulation software.
Offers optimized RMSD calculations that are 4x faster than traditional methods, leveraging vectorized operations for efficiency in large-scale analyses.
Includes a wide range of functions for bonds/angles/dihedrals, hydrogen bonding, secondary structure assignment, and NMR observables, covering common research needs without external tools.
Focuses on speed with a clean, vectorized API that minimizes overhead, making it suitable for scripting and batch processing in scientific workflows.
Primarily designed for numerical analysis and lacks built-in plotting or 3D visualization features, forcing users to rely on separate libraries like Matplotlib or VMD for graphical output.
Loads entire trajectories into memory by default, which can be prohibitive for very large datasets and requires manual optimization or chunking not detailed in the core documentation.
Assumes familiarity with molecular dynamics concepts and Python's scientific stack, making it less accessible for beginners or researchers without a computational background.