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Natural product-likeness score revisited: an open-source, open-data implementation

Overview of attention for article published in BMC Bioinformatics, January 2012
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (73rd percentile)
  • High Attention Score compared to outputs of the same age and source (83rd percentile)

Mentioned by

wikipedia
1 Wikipedia page
googleplus
1 Google+ user

Citations

dimensions_citation
33 Dimensions

Readers on

mendeley
80 Mendeley
citeulike
3 CiteULike
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Title
Natural product-likeness score revisited: an open-source, open-data implementation
Published in
BMC Bioinformatics, January 2012
DOI 10.1186/1471-2105-13-106
Pubmed ID
Authors

Kalai Vanii Jayaseelan, Pablo Moreno, Andreas Truszkowski, Peter Ertl, Christoph Steinbeck

Abstract

Natural product-likeness of a molecule, i.e. similarity of this molecule to the structure space covered by natural products, is a useful criterion in screening compound libraries and in designing new lead compounds. A closed source implementation of a natural product-likeness score, that finds its application in virtual screening, library design and compound selection, has been previously reported by one of us. In this note, we report an open-source and open-data re-implementation of this scoring system, illustrate its efficiency in ranking small molecules for natural product likeness and discuss its potential applications.

Mendeley readers

The data shown below were compiled from readership statistics for 80 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 3%
Italy 1 1%
Portugal 1 1%
Colombia 1 1%
South Africa 1 1%
United Kingdom 1 1%
China 1 1%
United States 1 1%
Unknown 71 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 20 25%
Researcher 15 19%
Student > Master 11 14%
Other 8 10%
Professor > Associate Professor 7 9%
Other 11 14%
Unknown 8 10%
Readers by discipline Count As %
Chemistry 21 26%
Agricultural and Biological Sciences 19 24%
Computer Science 13 16%
Biochemistry, Genetics and Molecular Biology 7 9%
Pharmacology, Toxicology and Pharmaceutical Science 4 5%
Other 8 10%
Unknown 8 10%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 30 November 2019.
All research outputs
#4,116,033
of 15,140,498 outputs
Outputs from BMC Bioinformatics
#1,766
of 5,567 outputs
Outputs of similar age
#32,355
of 126,334 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 6 outputs
Altmetric has tracked 15,140,498 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 5,567 research outputs from this source. They receive a mean Attention Score of 5.0. This one has gotten more attention than average, scoring higher than 67% 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 126,334 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them