CADENCE: Clustering Algorithm─Density-Based Exploration and Novelty Clustering with Efficiency

Unsupervised learning techniques play a pivotal role in unraveling protein folding landscapes, constructing Markov State Models, expediting replica exchange simulations, and discerning drug binding patterns, among other applications. A fundamental challenge in current clustering methods lies in how...

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Bibliographic Details
Published in:Journal of chemical information and modeling Vol. 65; no. 13; p. 6968
Main Authors: Chen, Lexin, Roe, Daniel R, Miranda-Quintana, Ramón Alain
Format: Journal Article
Language:English
Published: United States 14.07.2025
Subjects:
Algorithms
Cluster Analysis
Clustering Algorithms
Molecular Dynamics Simulation
Proteins - chemistry
Software
ISSN:1549-960X, 1549-960X
Online Access:Get more information
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Summary:Unsupervised learning techniques play a pivotal role in unraveling protein folding landscapes, constructing Markov State Models, expediting replica exchange simulations, and discerning drug binding patterns, among other applications. A fundamental challenge in current clustering methods lies in how similarities among objects are accessed. Traditional similarity operations are typically only defined over pairs of objects, and this limitation is at the core of many performance issues. The crux of the problem in this field is that efficient algorithms like -means struggle to distinguish between metastable states effectively. However, more robust methods like density-based clustering demand substantial computational resources. Extended similarity techniques have been proven to swiftly pinpoint high and low-density regions within the data in linear O( ) time. This offers a highly convenient means to explore complex conformational landscapes, enabling focused exploration of rare events or identification of the most representative conformations, such as the medoid of the data set. In this contribution, we aim to bridge this gap by introducing a novel density clustering algorithm to the Molecular Dynamics Analysis with -ary Clustering Ensembles (MDANCE) software package based on -ary similarity framework.
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ISSN:1549-960X
1549-960X
DOI:10.1021/acs.jcim.5c00392