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Confab - Systematic generation of diverse low-energy conformers

Overview of attention for article published in Journal of Cheminformatics, March 2011
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (91st percentile)
  • Good Attention Score compared to outputs of the same age and source (79th percentile)

Mentioned by

blogs
2 blogs
twitter
3 tweeters

Citations

dimensions_citation
85 Dimensions

Readers on

mendeley
132 Mendeley
citeulike
5 CiteULike
connotea
1 Connotea
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Title
Confab - Systematic generation of diverse low-energy conformers
Published in
Journal of Cheminformatics, March 2011
DOI 10.1186/1758-2946-3-8
Pubmed ID
Authors

Noel M O'Boyle, Tim Vandermeersch, Christopher J Flynn, Anita R Maguire, Geoffrey R Hutchison

Abstract

Many computational chemistry analyses require the generation of conformers, either on-the-fly, or in advance. We present Confab, an open source command-line application for the systematic generation of low-energy conformers according to a diversity criterion.

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 132 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 3 2%
United Kingdom 3 2%
United States 2 2%
Iran, Islamic Republic of 1 <1%
India 1 <1%
Russia 1 <1%
Spain 1 <1%
Ireland 1 <1%
Poland 1 <1%
Other 0 0%
Unknown 118 89%

Demographic breakdown

Readers by professional status Count As %
Researcher 37 28%
Student > Ph. D. Student 31 23%
Student > Master 10 8%
Student > Bachelor 9 7%
Other 8 6%
Other 20 15%
Unknown 17 13%
Readers by discipline Count As %
Chemistry 53 40%
Agricultural and Biological Sciences 22 17%
Biochemistry, Genetics and Molecular Biology 13 10%
Computer Science 8 6%
Physics and Astronomy 6 5%
Other 10 8%
Unknown 20 15%

Attention Score in Context

This research output has an Altmetric Attention Score of 15. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 22 May 2019.
All research outputs
#1,246,328
of 15,088,674 outputs
Outputs from Journal of Cheminformatics
#125
of 606 outputs
Outputs of similar age
#1,192,253
of 14,199,361 outputs
Outputs of similar age from Journal of Cheminformatics
#125
of 605 outputs
Altmetric has tracked 15,088,674 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 606 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.5. This one has done well, scoring higher than 79% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 14,199,361 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 91% of its contemporaries.
We're also able to compare this research output to 605 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 79% of its contemporaries.