Evidence map›Paper›PMID 22904601›Full record

ArticleMolecular simulation2012

Coarse-Grained Molecular Models of Water: A Review.

Kevin R Hadley, Clare McCabe

Abstract read
In one paragraph

Article in Molecular simulation, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing 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

21 citing papers in PubMed.

  1. Article
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  3. Article
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  5. Article
  6. Article
  7. Assembly and Analysis of Cell-Scale Membrane Envelopes.Journal of chemical information and modeling · 2022
    Article
  8. Article
  9. Article
  10. Review
  11. Article
  12. Article
  13. Review
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  16. Review
  17. Article
  18. Development of a coarse-grained water forcefield via multistate iterative Boltzmann inversion.Foundations of Molecular Modeling and Simulation : select papers from FOMMS 2015. International Conference on Foundations of Molecular Modeling and Simulation (6th : 2015 : Mount Hood, Or.) · 2016
    Article
  19. Article
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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

2 authors.

Kevin R HadleyNational Institute of Aerospace, Hampton, VA, 23666.
Clare McCabe

Funding

Using Molecular Modeling to Determine Structure and Organization in Skin LipidsR01AR057886 · NIAMS · VANDERBILT UNIVERSITY · PI MCCABE, CLARE · 2010 to 2012
$601k
Molecular Modeling of Self-Assembling Human Skin LipidsR21AR053270 · NIAMS · VANDERBILT UNIVERSITY · PI MCCABE, CLARE · 2007 to 2008
$370k
NIAMS NIH HHS R01 AR057886NIAMS NIH HHS R21 AR053270
6 · The paper itself

Abstract

Coarse-grained (CG) models have proven to be very effective tools in the study of phenomena or systems that involve large time- and length-scales. By decreasing the degrees of freedom in the system and using softer interactions than seen in atomistic models, larger timesteps can be used and much longer simulation times can be studied. CG simulations are widely used to study systems of biological importance that are beyond the reach of atomistic simulation, necessitating a computationally efficient and accurate CG model for water. In this review, we discuss the methods used for developing CG water models and the relative advantages and disadvantages of the resulting models. In general, CG water models differ with regards to how many waters each CG group or bead represents, whether analytical or tabular potentials have been used to describe the interactions, and how the model incorporates electrostatic interactions. Finally, how the models are parameterized depends on their application, so, while some are fitted to experimental properties such as surface tension and density, others are fitted to radial distribution functions extracted from atomistic simulations.

Identifiers

PMID22904601
PMCPMC3420348

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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.