The DOI matches its Crossref record; 45 of the 47 references it cites were found in a registry or an index.
Machine learning approaches for analyzing and enhancing molecular dynamics simulations
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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“Second, a number of groups in the molecular dynamics community are applying new machine learning methods to allow exploration of trajectories in less frustrated latent spaces30.”
From Critical assessment of methods of protein structure prediction (CASP)—Round XIV (2021) · cites this paper as [30]
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Self-citationPyEMMA 2: A Software Package for Estimation, Validation, and Analysis of Markov Models
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Critical assessment of methods of protein structure prediction (CASP)—Round XIV
Kryshtafovych, A., Schwede, T., Topf, M., Fidelis, K., & Moult, J. (2021) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26237 · Cited by 503
Recent Progress of Amorphous Nanomaterials
Kang, J., Yang, X., Hu, Q. L., Cai, Z. D., Liu, L., & Guo, L. (2023) · Chemical Reviews · doi:10.1021/acs.chemrev.3c00229 · Cited by 411
Enhanced Sampling Methods for Molecular Dynamics Simulations [Article v1.0]
Hénin, J., Tony Lelièvre, A., Shirts, M. R., Valsson, Ó., & Delemotte, L. (2022) · Living Journal of Computational Molecular Science · doi:10.33011/livecoms.4.1.1583 · Cited by 460
AI in drug discovery and its clinical relevance
Qureshi, R., Irfan, M., Gondal, T. M., Khan, S., Wu, J., Hadi, M. U., Heymach, J., Le, X., Yan, H., & Alam, T. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e17575 · Cited by 319
Bottom-up Coarse-Graining: Principles and Perspectives
Jin, J., Pak, A. J., Durumeric, A. E. P., Loose, T. D., & Voth, G. A. (2022) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c00643 · Cited by 321
Metadata
- Authors
- Wang, Y., Lamim Ribeiro, J. M., & Tiwary, P.
- Affiliations
- University of Maryland, College Park (US)
- Corresponding author
- Tiwary, Pratyush
- Venue
- Current Opinion in Structural Biology
- Publisher
- Elsevier BV
- Published
- 2020-04
- Volume
- 61
- Pages
- 139-145
- ISSN
- 0959-440X (ISSN-L)
- 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
- 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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