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A Tutorial on Dimensionality Reduction and Clustering for Molecular Dynamics Trajectories: From Linear Algorithms to

Xinyan Wu^1,2, Zhiteng Zhang^1,2, Donghui Shao^1,2

¹Research Center for Analytical Sciences, Tianjin Key Laboratory of Biosensing and Molecular Recognition, College of Chemistry, Nankai University, Tianjin 300071, China.

The Journal of Physical Chemistry. B

|July 15, 2026

Summary

This tutorial guides users in analyzing molecular dynamics (MD) simulations to find key states and slow motions. It covers dimensionality reduction, wavelet-enhanced, and deep learning methods for free energy landscape construction.

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Area of Science:

Computational Chemistry
Biophysics
Data Science

Background:

Molecular dynamics (MD) simulations generate large trajectory datasets.
Analyzing these trajectories is crucial for understanding molecular behavior.
Identifying metastable states and slow dynamics remains a challenge.

Purpose of the Study:

To provide a tutorial for analyzing MD simulation trajectories with minimal prior knowledge.
To enable users to identify key metastable and intermediate states.
To extract collective variables (CVs) and construct free energy landscapes.

Main Methods:

Linear dimensionality reduction: Principal Component Analysis (PCA) and Time-Lagged Independent Component Analysis (tICA).
Wavelet-enhanced methods: Wavelet-Enhanced tICA (WE-tICA) and Wavelet-Enhanced Time-Lagged Autoencoders (WE-tAE).
Deep learning-based methods: Deep Identification of Key Intermediates (DIKI).

Main Results:

Demonstrated application of methods to alanine tripeptide conformational transitions and CLN025 protein folding.
Successful identification of metastable states and extraction of slow collective degrees of freedom.
Validation of the broad applicability of presented methods across different molecular systems and MD engines.

Conclusions:

The tutorial offers a systematic approach to MD trajectory analysis.
The presented methods are effective for uncovering essential molecular dynamics.
These techniques are adaptable and broadly applicable in computational chemistry and biophysics.