Evidence map›Paper›PMID 29734794›Full record

ArticleInternational journal of molecular sciences2018

Molecular Surface Remeshing with Local Region Refinement.

Dawar Khan, Dong-Ming Yan, Sheng Gui, Benzhuo Lu, Xiaopeng Zhang

Abstract read
In one paragraph

Article in International journal of molecular sciences, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Frontiers in biomolecular mesh generation and molecular visualization systems.Visual computing for industry, biomedicine, and art · 2018
    Review
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

5 authors.

Dawar KhanNational Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China. dawar@ia.ac.cn.
Dong-Ming YanNational Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China. yandongming@gmail.com.ORCID 0000-0003-2209-2404
Sheng GuiUniversity of Chinese Academy of Sciences, Beijing 100049, China. shenggui@lsec.cc.ac.cn.
Benzhuo LuUniversity of Chinese Academy of Sciences, Beijing 100049, China. bzlu@lsec.cc.ac.cn.ORCID 0000-0003-4159-1532
Xiaopeng ZhangNational Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China. xiaopeng.zhang@ia.ac.cn.ORCID 0000-0002-0092-6474

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Molecular surface mesh generation is a prerequisite for using the boundary element method (BEM) and finite element method (FEM) in implicit-solvent modeling. Molecular surface meshes typically have small angles, redundant vertices, and low-quality elements. In the implicit-solvent modeling of biomolecular systems it is usually required to improve the mesh quality and eliminate low-quality elements. Existing methods often fail to efficiently remove low-quality elements, especially in complex molecular meshes. In this paper, we propose a mesh refinement method that smooths the meshes, eliminates invalid regions in a cut-and-fill strategy, and improves the minimal angle. We compared our method with four different state-of-the-art methods and found that our method showed a significant improvement over state-of-the-art methods in minimal angle, aspect ratio, and other meshing quality measurements. In addition, our method showed satisfactory results in terms of the ratio of regular vertices and the preservation of area and volume.

Indexed as

Finite Element AnalysisModels, MolecularModels, TheoreticalAlgorithmsComputer SimulationSolventsSurface PropertiesSolventsfinite element methodmeshing qualitymolecular simulationmolecular surface mesh

Identifiers

PMID29734794
PMCPMC5983798

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Registered trials

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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.