Evidence map›Paper›PMID 26183423›Full record

ArticleJournal of computational chemistry2016

Self-guided Langevin dynamics via generalized Langevin equation.

Xiongwu Wu, Bernard R Brooks, Eric Vanden-Eijnden

Abstract read
In one paragraph

Article in Journal of computational chemistry, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.

0numbers the graph read from it
0cells of the map it votes in
33citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

33 citing papers in PubMed.

  1. Article
  2. Self-Guided Molecular Simulation Methods.International journal of molecular sciences · 2025
    Review
  3. Recent Developments in Amber Biomolecular Simulations.Journal of chemical information and modeling · 2025
    Article
  4. Article
  5. Article
  6. Article
  7. Intrinsic disorder and conformational coexistence in auxin coreceptors.Proceedings of the National Academy of Sciences of the United States of America · 2023
    Article
  8. Article
  9. Article
  10. Article
  11. Gaussian Accelerated Molecular Dynamics in OpenMM.The journal of physical chemistry. B · 2022
    Article
  12. Article
  13. Article
  14. Article
  15. Binding Analysis Using Accelerated Molecular Dynamics Simulations and Future Perspectives.Advances and applications in bioinformatics and chemistry : AABC · 2022
    Review
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Xiongwu WuLaboratory of Computational Biology, National Heart, Lung, and Blood Institute (NHLBI), National Institutes of Health (NIH), Bethesda, Maryland, 20892.
Bernard R BrooksLaboratory of Computational Biology, National Heart, Lung, and Blood Institute (NHLBI), National Institutes of Health (NIH), Bethesda, Maryland, 20892.
Eric Vanden-EijndenCourant Institute of Mathematical Sciences, New York University, New York, New York, 10012.

Funding

Development Of Theoretical Methods For Studying Biological MacromoleculesZIAHL001051 · NHLBI · NATIONAL HEART, LUNG, AND BLOOD INSTITUTE · PI BROOKS, BERNARD R · 2009 to 2025
$16.2M
Transition Path Theory and Markovian Milestoning for Prediction of Protein-Ligand Binding Kinetics in Molecular SimulationsR01GM100472 · NIGMS · DREXEL UNIVERSITY · PI ABRAMS, CAMERON F · 2011 to 2020
$2.5M
Intramural NIH HHS Z99 HL999999NIGMS NIH HHS R01 GM100472PHS HHS N00014-11-1-0345
6 · The paper itself

Abstract

Self-guided Langevin dynamics (SGLD) is a molecular simulation method that enhances conformational search and sampling via acceleration of the low frequency motions of the system. This acceleration is produced via introduction of a guiding force which breaks down the detailed-balance property of the dynamics, implying that some reweighting is necessary to perform equilibrium sampling. Here, we eliminate the need of reweighing and show that the NVT and NPT ensembles are sampled exactly by a new version of self-guided motion involving a generalized Langevin equation (GLE) in which the random force is modified so as to restore detailed-balance. Through the examples of alanine dipeptide and argon liquid, we show that this SGLD-GLE method has enhanced conformational sampling capabilities compared with regular Langevin dynamics (LD) while being of comparable computational complexity. In particular, SGLD-GLE is fully size extensive and can be used in arbitrarily large systems, making it an appealing alternative to LD. © 2015 Wiley Periodicals, Inc.

Indexed as

Molecular Dynamics SimulationAlgorithmsArgonDipeptidesThermodynamicsalanylalanineArgonDipeptidescanonical ensembleconformational samplinggeneralized Langevin equationmolecular simulationself-guided Langevin dynamics

Identifiers

PMID26183423
PMCPMC4715807

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.