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Deep learning enables rapid identification of potent DDR1 kinase inhibitors

Overview of attention for article published in Nature Biotechnology, September 2019
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • One of the highest-scoring outputs from this source (#9 of 7,686)
  • High Attention Score compared to outputs of the same age (99th percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Citations

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281 Dimensions

Readers on

mendeley
914 Mendeley
Title
Deep learning enables rapid identification of potent DDR1 kinase inhibitors
Published in
Nature Biotechnology, September 2019
DOI 10.1038/s41587-019-0224-x
Pubmed ID
Authors

Alex Zhavoronkov, Yan A. Ivanenkov, Alex Aliper, Mark S. Veselov, Vladimir A. Aladinskiy, Anastasiya V. Aladinskaya, Victor A. Terentiev, Daniil A. Polykovskiy, Maksim D. Kuznetsov, Arip Asadulaev, Yury Volkov, Artem Zholus, Rim R. Shayakhmetov, Alexander Zhebrak, Lidiya I. Minaeva, Bogdan A. Zagribelnyy, Lennart H. Lee, Richard Soll, David Madge, Li Xing, Tao Guo, Alán Aspuru-Guzik

Twitter Demographics

The data shown below were collected from the profiles of 1,565 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 914 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 914 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 230 25%
Student > Ph. D. Student 172 19%
Student > Master 97 11%
Student > Bachelor 79 9%
Other 43 5%
Other 101 11%
Unknown 192 21%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 154 17%
Chemistry 143 16%
Computer Science 104 11%
Agricultural and Biological Sciences 69 8%
Engineering 47 5%
Other 172 19%
Unknown 225 25%

Attention Score in Context

This research output has an Altmetric Attention Score of 1606. 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 01 December 2021.
All research outputs
#4,249
of 19,622,294 outputs
Outputs from Nature Biotechnology
#9
of 7,686 outputs
Outputs of similar age
#69
of 279,115 outputs
Outputs of similar age from Nature Biotechnology
#1
of 86 outputs
Altmetric has tracked 19,622,294 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,686 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 36.8. This one has done particularly well, scoring higher than 99% 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 279,115 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 99% of its contemporaries.
We're also able to compare this research output to 86 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 99% of its contemporaries.