Machine learning in the analysis of biomolecular simulations

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http://hdl.handle.net/10138/338859

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Kaptan , S & Vattulainen , I 2022 , ' Machine learning in the analysis of biomolecular simulations ' , Advances in physics: X , vol. 7 , no. 1 , 2006080 . https://doi.org/10.1080/23746149.2021.2006080

Title: Machine learning in the analysis of biomolecular simulations
Author: Kaptan, Shreyas; Vattulainen, Ilpo
Contributor organization: Materials Physics
Department of Physics
Doctoral Programme in Biomedicine
Doctoral Programme in Chemistry and Molecular Sciences
Doctoral Programme in Integrative Life Science
Doctoral Programme in Materials Research and Nanosciences
Date: 2022-12-31
Language: eng
Number of pages: 31
Belongs to series: Advances in physics: X
ISSN: 2374-6149
DOI: https://doi.org/10.1080/23746149.2021.2006080
URI: http://hdl.handle.net/10138/338859
Abstract: Machine learning has rapidly become a key method for the analysis and organization of large-scale data in all scientific disciplines. In life sciences, the use of machine learning techniques is a particularly appealing idea since the enormous capacity of computational infrastructures generates terabytes of data through millisecond simulations of atomistic and molecular-scale biomolecular systems. Due to this explosion of data, the automation, reproducibility, and objectivity provided by machine learning methods are highly desirable features in the analysis of complex systems. In this review, we focus on the use of machine learning in biomolecular simulations. We discuss the main categories of machine learning tasks, such as dimensionality reduction, clustering, regression, and classification used in the analysis of simulation data. We then introduce the most popular classes of techniques involved in these tasks for the purpose of enhanced sampling, coordinate discovery, and structure prediction. Whenever possible, we explain the scope and limitations of machine learning approaches, and we discuss examples of applications of these techniques.
Subject: Biomolecular simulations
molecular dynamics
machine learning
deep learning
biophysics
MOLECULAR-DYNAMICS TRAJECTORIES
PARTIAL LEAST-SQUARES
PRINCIPAL COMPONENT
ORDER PARAMETERS
CLUSTER-ANALYSIS
DIMENSIONALITY
METADYNAMICS
LANDSCAPES
PROTEINS
MIXTURE
114 Physical sciences
Peer reviewed: Yes
Rights: cc_by
Usage restriction: openAccess
Self-archived version: publishedVersion
Funder: EU Marie Curie IIF
Grant number: 101033606


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