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Ambiguity of non-systematic chemical identifiers within and between small-molecule databases

Overview of attention for article published in Journal of Cheminformatics, November 2015
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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 (88th percentile)
  • Good Attention Score compared to outputs of the same age and source (77th percentile)

Mentioned by

blogs
1 blog
twitter
10 tweeters

Citations

dimensions_citation
7 Dimensions

Readers on

mendeley
36 Mendeley
citeulike
2 CiteULike
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Title
Ambiguity of non-systematic chemical identifiers within and between small-molecule databases
Published in
Journal of Cheminformatics, November 2015
DOI 10.1186/s13321-015-0102-6
Pubmed ID
Authors

Saber A. Akhondi, Sorel Muresan, Antony J. Williams, Jan A. Kors

Abstract

A wide range of chemical compound databases are currently available for pharmaceutical research. To retrieve compound information, including structures, researchers can query these chemical databases using non-systematic identifiers. These are source-dependent identifiers (e.g., brand names, generic names), which are usually assigned to the compound at the point of registration. The correctness of non-systematic identifiers (i.e., whether an identifier matches the associated structure) can only be assessed manually, which is cumbersome, but it is possible to automatically check their ambiguity (i.e., whether an identifier matches more than one structure). In this study we have quantified the ambiguity of non-systematic identifiers within and between eight widely used chemical databases. We also studied the effect of chemical structure standardization on reducing the ambiguity of non-systematic identifiers. The ambiguity of non-systematic identifiers within databases varied from 0.1 to 15.2 % (median 2.5 %). Standardization reduced the ambiguity only to a small extent for most databases. A wide range of ambiguity existed for non-systematic identifiers that are shared between databases (17.7-60.2 %, median of 40.3 %). Removing stereochemistry information provided the largest reduction in ambiguity across databases (median reduction 13.7 percentage points). Ambiguity of non-systematic identifiers within chemical databases is generally low, but ambiguity of non-systematic identifiers that are shared between databases, is high. Chemical structure standardization reduces the ambiguity to a limited extent. Our findings can help to improve database integration, curation, and maintenance.

Twitter Demographics

The data shown below were collected from the profiles of 10 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 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 3%
Brazil 1 3%
Sweden 1 3%
United Kingdom 1 3%
United States 1 3%
Unknown 31 86%

Demographic breakdown

Readers by professional status Count As %
Researcher 13 36%
Student > Ph. D. Student 6 17%
Student > Master 6 17%
Other 5 14%
Student > Bachelor 3 8%
Other 1 3%
Unknown 2 6%
Readers by discipline Count As %
Chemistry 12 33%
Computer Science 5 14%
Agricultural and Biological Sciences 4 11%
Biochemistry, Genetics and Molecular Biology 3 8%
Medicine and Dentistry 3 8%
Other 4 11%
Unknown 5 14%

Attention Score in Context

This research output has an Altmetric Attention Score of 14. 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 15 February 2016.
All research outputs
#926,052
of 12,010,397 outputs
Outputs from Journal of Cheminformatics
#98
of 467 outputs
Outputs of similar age
#25,922
of 231,068 outputs
Outputs of similar age from Journal of Cheminformatics
#4
of 18 outputs
Altmetric has tracked 12,010,397 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 467 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.9. 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 231,068 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 88% of its contemporaries.
We're also able to compare this research output to 18 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 77% of its contemporaries.