The DOI matches its Crossref record; 71 of the 73 references it cites were found in a registry or an index.
Intrinsic map dynamics exploration for uncharted effective free-energy landscapes
Open accessAbstract
Significance: Direct simulations explore the dynamics of physical systems at their natural pace. Molecular dynamics (MD) simulations (e.g., of macromolecular folding) extensively revisit typical configurations until rare and interesting transition events occur. Biasing the simulator away from regions already explored can, therefore, drastically accelerate the discovery of features. We propose an enhanced sampling simulation framework, where MD and machine learning adaptively bootstrap each other. Machine learning guides the search for important configurations by processing information from previous explorations. This search proceeds iteratively in an algorithmically orchestrated fashion without advance knowledge of suitable collective variables. Applied to a molecular sensor of lipid saturation in membranes, a helix dissociation pathway not seen in millisecond simulations is discovered at the second iteration.
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“The challenge of identifying the most appropriate low-dimensional space to accelerate conformational sampling is reflected in the fact that most of early (and recent) applications of dimensionality reduction to atomistic modeling involved simulations of rare events or phase transitions 71,72,95-98 .”
From Unsupervised machine learning in atomistic simulations, between predictions and understanding (2019) · cites this paper as [98]
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Cites
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Molecular dynamics with coupling to an external bath
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Research works printed but not linked: 2
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- C Dsilva Manifold learning for dynamical systems. PhD thesis (Princeton University Princeton). (2015).
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Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery
Yang, X. Y., Wang, Y., Byrne, R., Schneider, G., & Yang, S. (2019) · Chemical Reviews · doi:10.1021/acs.chemrev.8b00728 · Cited by 1,079
Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems
Keith, J. A., Vassilev-Galindo, V., Cheng, B., Chmiela, S., Gastegger, M., Müller, K.-R., & Tkatchenko, A. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00107 · Cited by 849
Machine-learned potentials for next-generation matter simulations
Friederich, P., Häse, F., Proppe, J., & Aspuru‐Guzik, A. (2021) · Nature Materials · doi:10.1038/s41563-020-0777-6 · Cited by 595
“Finally, machine learning models can also be used to enhance the sampling of MD simulations by learning bias potentials 147 and thus enable more efficient exploration of the configuration space based on data analysis 148 .”
MethodUnsupervised Learning Methods for Molecular Simulation Data
Glielmo, A., Husic, B. E., Rodríguez, Á., Clementi, C., Noé, F., & Laio, A. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.0c01195 · Cited by 436
“Di ff usion map has been applied to analyze the slow transitions of molecules, 137,138 guide enhanced-sampling methods, 139 − 141 and have been combined with the kinetic variational principles described in section 6.”
MethodEnhanced 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
“iMapD relies on clustering in a low-dimensional manifold inferred by dimensionality reduction and selecting states to seed from the boundaries of a diffusion map in diffusion coordinates [18] 9.”
BackgroundMethod
Metadata
- Authors
- Chiavazzo, E., Covino, R., Coifman, R. R., Gear, C. W., Georgiou, A. S., Hummer, G., & Kevrekidis, I. G.
- Affiliations
- Goethe University Frankfurt (DE)
- Politecnico di Torino (IT)
- ORCID
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2017-06-20
- Volume
- 114
- Issue
- 28
- Pages
- E5494-E5503
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC5514710
- OpenAlex
- W2571642833
- Cited by
- 125
- References
- 73
- Citations by year
- 2026: 2 · 2025: 10 · 2024: 5 · 2023: 17 · 2022: 14 · 2021: 11 · 2020: 23 · 2019: 18 · 2018: 16 · 2017: 9
- FWCI
- 7.37
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- PORTO@Iris (submittedVersion, other-oa)
- PORTO Publications Open Repository TOrino (Politecnico di Torino) (submittedVersion, other-oa)
- Max Planck Digital Library (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Funders
- Ministero dell’Istruzione, dell’Università e della Ricerca (2012LHPSJC)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Chemistry › Physical and Theoretical Chemistry › Advanced Physical and Chemical Molecular Interactions
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Keywords
- enhanced sampling, free energy landscape, machine learning, molecular dynamics, adaptive biasing, collective variables, lipid saturation
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