PyL3dMD: Python LAMMPS 3D molecular descriptors package.

Area of Science:

• Computational Chemistry
• Materials Science
• Cheminformatics

Background:

• Molecular descriptors are crucial for understanding molecular properties and interactions.
• Three-dimensional (3D) descriptors derived from molecular geometry capture physical and chemical characteristics.
• Calculating 3D descriptors from molecular dynamics (MD) simulations can incorporate environmental factors like temperature and pressure.

Purpose of the Study:

• To introduce PyL3dMD, an open-source Python package for calculating 3D molecular descriptors from MD simulation trajectories.
• To enable researchers to easily extract a wide range of 3D descriptors for advanced quantitative-structure-property-relationship (QSPR) modeling.
• To address the non-trivial challenges in extracting 3D descriptors from MD data.

Main Methods:

• Developed PyL3dMD, a suite of Python-based post-processing routines.
• Ensured compatibility with the LAMMPS molecular dynamics simulation package.
• Enabled computation of over 2000 distinct 3D molecular descriptors from atomic trajectories.

Main Results:

• PyL3dMD is compatible with major platforms (Windows, Linux, macOS) and easily installable via GitHub.
• A performance benchmark demonstrated PyL3dMD's speed and efficiency for large, complex systems and long simulations.
• Descriptors generated by PyL3dMD were successfully used to develop a neural network model.

Conclusions:

• PyL3dMD simplifies the extraction of 3D molecular descriptors from MD simulations.
• The tool enhances the application of MD-derived descriptors in machine learning for cheminformatics and materials design.
• PyL3dMD is a valuable, flexible, and efficient open-source resource for the scientific community.