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In silico prediction of UGT-mediated metabolism in drug-like molecules via graph neural network

Overview of attention for article published in Journal of Cheminformatics, July 2022
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (59th percentile)

Mentioned by

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5 X users

Citations

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5 Dimensions

Readers on

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19 Mendeley
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Title
In silico prediction of UGT-mediated metabolism in drug-like molecules via graph neural network
Published in
Journal of Cheminformatics, July 2022
DOI 10.1186/s13321-022-00626-3
Pubmed ID
Authors

Mengting Huang, Chaofeng Lou, Zengrui Wu, Weihua Li, Philip W. Lee, Yun Tang, Guixia Liu

X Demographics

X Demographics

The data shown below were collected from the profiles of 5 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 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 19 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 16%
Unspecified 1 5%
Researcher 1 5%
Student > Doctoral Student 1 5%
Student > Master 1 5%
Other 0 0%
Unknown 12 63%
Readers by discipline Count As %
Chemistry 2 11%
Unspecified 1 5%
Computer Science 1 5%
Biochemistry, Genetics and Molecular Biology 1 5%
Unknown 14 74%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 13 July 2022.
All research outputs
#13,527,742
of 23,344,526 outputs
Outputs from Journal of Cheminformatics
#673
of 862 outputs
Outputs of similar age
#173,954
of 437,537 outputs
Outputs of similar age from Journal of Cheminformatics
#25
of 27 outputs
Altmetric has tracked 23,344,526 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 862 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.0. This one is in the 20th percentile – i.e., 20% of its peers scored the same or lower than it.
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 437,537 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 59% of its contemporaries.
We're also able to compare this research output to 27 others from the same source and published within six weeks on either side of this one. This one is in the 11th percentile – i.e., 11% of its contemporaries scored the same or lower than it.