LPATH: A Semiautomated Python Tool for Clustering Molecular Pathways.

Area of Science:

• Computational Chemistry
• Biophysics
• Machine Learning

Background:

• Analyzing molecular pathways is crucial for understanding transition mechanisms.
• Pathway analysis is challenging due to diversity and variable lengths.
• Existing methods struggle with large datasets and complex pathway structures.

Purpose of the Study:

• To introduce LPATH, a novel Python tool for semiautomated pathway clustering.
• To facilitate the analysis of molecular dynamics simulation pathways.
• To enable efficient classification of diverse and variable-length pathways.

Main Methods:

• Linguistics-assisted clustering of pathways into distinct classes.
• Discretizing configurational space into key states.
• Extracting text-string sequences of visited states for pathway matching.
• Employing a two-stage machine learning method for conformational state clustering to manage memory requirements.

Main Results:

• LPATH successfully implements a linguistics-assisted clustering method.
• The tool effectively categorizes pathways into physically reasonable classes.
• Demonstrated utility in analyzing the C7eq to C7ax conformational transition of alanine dipeptide, providing accurate probabilities.

Conclusions:

• LPATH offers an efficient and scalable solution for molecular pathway analysis.
• The tool aids in understanding complex molecular processes by simplifying pathway data.
• LPATH is compatible with WESTPA and conventional simulations, broadening its applicability.