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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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Torrie, G. M., & Valleau, J. P. (1977) · Journal of Computational Physics · doi:10.1016/0021-9991(77)90121-8 · Cited by 6,091
Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces
Behler, J., & Parrinello, M. (2007) · Physical Review Letters · doi:10.1103/physrevlett.98.146401 · Cited by 5,010
Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons
Bartók, A. P., Payne, M. C., Kondor, R., & Csányi, G. (2010) · Physical Review Letters · doi:10.1103/physrevlett.104.136403 · Cited by 3,242
Density estimation using Real NVP
Dinh, L., Sohl-Dickstein, J., & Bengio, S. (2016) · arXiv · Cited by 2,067
ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost
Smith, J. S., Isayev, O., & Roitberg, A. E. (2017) · Chemical Science · doi:10.1039/c6sc05720a · Cited by 1,921
Geometric diffusions as a tool for harmonic analysis and structure definition of data: Diffusion maps
Coifman, R. R., Lafon, S., Lee, A. B., Maggioni, M., Nadler, B., Warner, F., & Zucker, S. W. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0500334102 · Cited by 1,675
Promoting transparency and reproducibility in enhanced molecular simulations
Bonomi, M., Bussi, G., Bernetti, M., Cesari, A., Laio, A., Camilloni, C., Paissoni, C., Tribello, G. A., Banáš, P., Barducci, A., et al. (2019) · Nature Methods · doi:10.1038/s41592-019-0506-8 · Cited by 1,193
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Identification of slow molecular order parameters for Markov model construction
Pérez-Hernández, G., Paul, F., Giorgino, T., De Fabritiis, G., & Noé, F. (2013) · The Journal of Chemical Physics · doi:10.1063/1.4811489 · Cited by 990 · cited 2×
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Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning
Noé, F., Olsson, S., Köhler, J., & Wu, H. (2019) · Science · doi:10.1126/science.aaw1147 · Cited by 863

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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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