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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
  • Among the highest-scoring outputs from this source (#11 of 8,607)
  • High Attention Score compared to outputs of the same age (99th percentile)
  • High Attention Score compared to outputs of the same age and source (98th percentile)

Citations

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721 Dimensions

Readers on

mendeley
1298 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

X Demographics

X Demographics

The data shown below were collected from the profiles of 1,451 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 1,298 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 1298 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 286 22%
Student > Ph. D. Student 215 17%
Student > Master 119 9%
Student > Bachelor 101 8%
Other 66 5%
Other 142 11%
Unknown 369 28%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 192 15%
Chemistry 184 14%
Computer Science 125 10%
Agricultural and Biological Sciences 75 6%
Pharmacology, Toxicology and Pharmaceutical Science 64 5%
Other 237 18%
Unknown 421 32%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1598. 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 11 April 2024.
All research outputs
#7,069
of 25,837,817 outputs
Outputs from Nature Biotechnology
#11
of 8,607 outputs
Outputs of similar age
#107
of 352,883 outputs
Outputs of similar age from Nature Biotechnology
#1
of 97 outputs
Altmetric has tracked 25,837,817 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 8,607 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 44.7. 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 352,883 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 97 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 98% of its contemporaries.