MonicaMD: Molecules and Internal Cluster Analysis of Molecular Dynamics Simulations

Area of Science:

• Computational Chemistry
• Molecular Modeling
• Biophysics

Background:

• Molecular dynamics (MD) simulations are crucial for studying molecular systems but generate complex, high-dimensional data.
• Extracting detailed mechanistic insights from MD trajectories remains a significant challenge.

Purpose of the Study:

• To introduce MonicaMD, a versatile software package for analyzing molecular dynamics simulations.
• To provide a user-friendly tool for extracting structural and electrostatic information from molecular trajectories.
• To facilitate the integration of classical MD with higher-accuracy quantum mechanics methods.

Main Methods:

• Development of the MonicaMD program package for classical and semiclassical MD trajectories.
• Modular access to structural and electrostatic information, including internal and collective variables.
• Implementation of dimensionality reduction, automatic feature-space generation, and templating for quantum chemistry and machine learning frameworks.

Main Results:

• MonicaMD enables efficient analysis of molecules and molecular clusters in atomistic simulations.
• The software provides a bridge between classical MD and quantum mechanics, demonstrated through various case studies.
• Applications include protein-ligand interactions, DNA intercalation, photoswitch isomerization, and catalytic reactions.

Conclusions:

• MonicaMD offers a powerful and accessible solution for analyzing complex MD simulation data.
• The tool enhances the synergy between classical and quantum mechanical simulations.
• It facilitates deeper mechanistic understanding across diverse chemical and biological systems.