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HydraScreen: A Generalizable Structure-Based Deep Learning Approach to Drug Discovery

Overview of attention for article published in Journal of Chemical Information and Modeling, July 2024
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (76th percentile)
  • Good Attention Score compared to outputs of the same age and source (73rd percentile)

Mentioned by

twitter
19 X users

Citations

dimensions_citation
1 Dimensions

Readers on

mendeley
13 Mendeley
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Title
HydraScreen: A Generalizable Structure-Based Deep Learning Approach to Drug Discovery
Published in
Journal of Chemical Information and Modeling, July 2024
DOI 10.1021/acs.jcim.4c00481
Pubmed ID
Authors

Alvaro Prat, Hisham Abdel Aty, Orestis Bastas, Gintautas Kamuntavičius, Tanya Paquet, Povilas Norvaišas, Piero Gasparotto, Roy Tal

Timeline

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X Demographics

X Demographics

The data shown below were collected from the profiles of 19 X users who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 13 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 31%
Student > Ph. D. Student 2 15%
Lecturer 2 15%
Student > Doctoral Student 1 8%
Unknown 4 31%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 3 23%
Business, Management and Accounting 1 8%
Agricultural and Biological Sciences 1 8%
Physics and Astronomy 1 8%
Medicine and Dentistry 1 8%
Other 0 0%
Unknown 6 46%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 27 August 2024.
All research outputs
#5,795,314
of 26,746,748 outputs
Outputs from Journal of Chemical Information and Modeling
#1,913
of 6,182 outputs
Outputs of similar age
#69,882
of 299,224 outputs
Outputs of similar age from Journal of Chemical Information and Modeling
#47
of 189 outputs
Altmetric has tracked 26,746,748 research outputs across all sources so far. Compared to these this one has done well and is in the 78th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,182 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.9. This one has gotten more attention than average, scoring higher than 68% 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 299,224 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 76% of its contemporaries.
We're also able to compare this research output to 189 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 73% of its contemporaries.