Cited by 303Protein Structure and DynamicsComputational Drug Discovery MethodsMachine Learning in Materials Science

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Machine learning approaches for analyzing and enhancing molecular dynamics simulations

Wang, Yihang, Lamim Ribeiro, João Marcelo, Tiwary, Pratyush·2020·Current Opinion in Structural Biology

DOI 10.1016/j.sbi.2019.12.016

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Abstract

Molecular dynamics (MD) has become a powerful tool for studying biophysical systems, due to increasing computational power and availability of software. Although MD has made many contributions to better understanding these complex biophysical systems, there remain methodological difficulties to be surmounted. First, how to make the deluge of data generated in running even a microsecond long MD simulation human comprehensible. Second, how to efficiently sample the underlying free energy surface and kinetics. In this short perspective, we summarize machine learning based ideas that are solving both of these limitations, with a focus on their key theoretical underpinnings and remaining challenges.

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Authors: Wang, Y., Lamim Ribeiro, J. M., & Tiwary, P.
Affiliations: University of Maryland, College Park (US)
Corresponding author: Tiwary, Pratyush
ORCID: Wang, Yihang
Tiwary, Pratyush
Venue: Current Opinion in Structural Biology
Publisher: Elsevier BV
Published: 2020-04
Volume: 61
Pages: 139-145
ISSN: 0959-440X (ISSN-L)
DOI: 10.1016/j.sbi.2019.12.016
OpenAlex: W3000999892
Cited by: 303
References: 47
Citations by year: 2026: 36 · 2025: 53 · 2024: 49 · 2023: 61 · 2022: 49 · 2021: 35 · 2020: 20
FWCI: 3.95
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
Locations: Current Opinion in Structural Biology (publishedVersion)
PubMed (publishedVersion)
Funders: NCI-UMD Partnership for Integrative Cancer Research
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
Keywords: machine learning, molecular dynamics simulation, free energy landscape, reaction kinetics, sampling efficiency, biophysical systems
MeSH terms: Machine Learning, Algorithms, Models, Theoretical, Software, Neural Networks, Computer, Molecular Dynamics Simulation

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Machine learning approaches for analyzing and enhancing molecular dynamics simulations

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