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Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

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Adaptive partitioning by local density-peaks: An efficient density-based clustering algorithm for analyzing molecular

Song Liu¹, Lizhe Zhu^1,2, Fu Kit Sheong¹

¹Department of Chemistry, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong.

Journal of Computational Chemistry

|November 22, 2016

Summary

We developed APLoD, an efficient clustering method for molecular dynamics (MD) trajectories. This adaptive-resolution approach significantly speeds up analysis and memory usage for large datasets.

Keywords:

Markov State Models clustering algorithm density peaks kNN search molecular dynamics

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Spatial Separation of Molecular Conformers and Clusters

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Published on: January 9, 2014

Area of Science:

Computational Chemistry
Biophysics
Data Analysis

Background:

Analyzing large-scale molecular dynamics (MD) trajectories is computationally intensive.
Existing clustering algorithms often struggle with efficiency and adaptability for complex systems.

Purpose of the Study:

To introduce APLoD, a novel density-based adaptive-resolution clustering method.
To demonstrate APLoD's efficiency and adaptability in analyzing MD trajectories.

Main Methods:

APLoD utilizes a k-nearest-neighbors search for local density estimation.
It groups conformations within high-density regions into clusters.
The method adapts cluster size based on local conformational density.

Main Results:

APLoD reduces running time and memory usage by 2-3 orders of magnitude compared to existing methods.
It successfully analyzed systems from small molecules (alanine dipeptide) to large proteins (370-residue Maltose-binding protein).
APLoD generates adaptive clusters, larger in low-density and smaller in high-density regions.

Conclusions:

APLoD offers a significant improvement over traditional clustering algorithms like k-centers and k-medoids.
The method is highly efficient for large-scale MD data analysis.
APLoD is anticipated to be valuable for constructing Markov State Models from massive MD datasets.