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An algorithm to identify functional groups in organic molecules

Overview of attention for article published in Journal of Cheminformatics, June 2017
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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)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

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36 X users
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3 Google+ users
reddit
1 Redditor

Citations

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78 Dimensions

Readers on

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217 Mendeley
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Title
An algorithm to identify functional groups in organic molecules
Published in
Journal of Cheminformatics, June 2017
DOI 10.1186/s13321-017-0225-z
Pubmed ID
Authors

Peter Ertl

Abstract

The concept of functional groups forms a basis of organic chemistry, medicinal chemistry, toxicity assessment, spectroscopy and also chemical nomenclature. All current software systems to identify functional groups are based on a predefined list of substructures. We are not aware of any program that can identify all functional groups in a molecule automatically. The algorithm presented in this article is an attempt to solve this scientific challenge. An algorithm to identify functional groups in a molecule based on iterative marching through its atoms is described. The procedure is illustrated by extracting functional groups from the bioactive portion of the ChEMBL database, resulting in identification of 3080 unique functional groups. A new algorithm to identify all functional groups in organic molecules is presented. The algorithm is relatively simple and full details with examples are provided, therefore implementation in any cheminformatics toolkit should be relatively easy. The new method allows the analysis of functional groups in large chemical databases in a way that was not possible using previous approaches. Graphical abstract .

X Demographics

X Demographics

The data shown below were collected from the profiles of 36 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 217 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 <1%
Germany 1 <1%
Unknown 215 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 39 18%
Researcher 39 18%
Student > Master 29 13%
Student > Bachelor 18 8%
Other 9 4%
Other 30 14%
Unknown 53 24%
Readers by discipline Count As %
Chemistry 67 31%
Biochemistry, Genetics and Molecular Biology 22 10%
Computer Science 14 6%
Pharmacology, Toxicology and Pharmaceutical Science 11 5%
Engineering 7 3%
Other 37 17%
Unknown 59 27%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 28. 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 09 September 2021.
All research outputs
#1,310,071
of 24,224,854 outputs
Outputs from Journal of Cheminformatics
#74
of 891 outputs
Outputs of similar age
#26,849
of 321,158 outputs
Outputs of similar age from Journal of Cheminformatics
#3
of 17 outputs
Altmetric has tracked 24,224,854 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 891 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.5. This one has done particularly well, scoring higher than 91% 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 321,158 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 17 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.