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Assessing the information content of structural and protein–ligand interaction representations for the classification of kinase inhibitor binding modes via machine learning and active learning

Overview of attention for article published in Journal of Cheminformatics, May 2020
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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 (83rd percentile)
  • Good Attention Score compared to outputs of the same age and source (68th percentile)

Mentioned by

blogs
1 blog
twitter
11 X users

Citations

dimensions_citation
17 Dimensions

Readers on

mendeley
41 Mendeley
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Title
Assessing the information content of structural and protein–ligand interaction representations for the classification of kinase inhibitor binding modes via machine learning and active learning
Published in
Journal of Cheminformatics, May 2020
DOI 10.1186/s13321-020-00434-7
Pubmed ID
Authors

Raquel Rodríguez-Pérez, Filip Miljković, Jürgen Bajorath

X Demographics

X Demographics

The data shown below were collected from the profiles of 11 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 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 41 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 20%
Student > Ph. D. Student 7 17%
Student > Master 4 10%
Student > Doctoral Student 3 7%
Lecturer 1 2%
Other 2 5%
Unknown 16 39%
Readers by discipline Count As %
Chemistry 5 12%
Computer Science 5 12%
Biochemistry, Genetics and Molecular Biology 4 10%
Pharmacology, Toxicology and Pharmaceutical Science 3 7%
Engineering 2 5%
Other 4 10%
Unknown 18 44%
Attention Score in Context

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 29 September 2021.
All research outputs
#2,581,497
of 25,074,338 outputs
Outputs from Journal of Cheminformatics
#234
of 943 outputs
Outputs of similar age
#65,211
of 397,921 outputs
Outputs of similar age from Journal of Cheminformatics
#8
of 22 outputs
Altmetric has tracked 25,074,338 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 943 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.2. This one has done well, scoring higher than 75% 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 397,921 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 83% of its contemporaries.
We're also able to compare this research output to 22 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.