Report for: Deep learning enables rapid identification of potent DDR1 kinase inhibitors

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	31477924
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

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Twitter Demographics

The data shown below were collected from the profiles of 1,472 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Geographical breakdown

Country	Count	As %
United States	260	18%
United Kingdom	56	4%
Russia	49	3%
India	47	3%
France	35	2%
Canada	31	2%
Germany	20	1%
Spain	20	1%
Japan	19	1%
Other	227	15%
Unknown	708	48%

Demographic breakdown

Type	Count	As %
Members of the public	1159	79%
Scientists	250	17%
Practitioners (doctors, other healthcare professionals)	33	2%
Science communicators (journalists, bloggers, editors)	27	2%
Unknown	3	<1%

Mendeley readers

The data shown below were compiled from readership statistics for 1,106 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country	Count	As %
Unknown	1106	100%

Demographic breakdown

Readers by professional status	Count	As %
Researcher	268	24%
Student > Ph. D. Student	195	18%
Student > Master	108	10%
Student > Bachelor	95	9%
Other	49	4%
Other	116	10%
Unknown	275	25%

Readers by discipline	Count	As %
Biochemistry, Genetics and Molecular Biology	176	16%
Chemistry	166	15%
Computer Science	117	11%
Agricultural and Biological Sciences	71	6%
Pharmacology, Toxicology and Pharmaceutical Science	58	5%
Other	200	18%
Unknown	318	29%

Attention Score in Context

This research output has an Altmetric Attention Score of 1576. 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 17 February 2023.

All research outputs

#6,128

of 23,393,453 outputs

Outputs from Nature Biotechnology

#12

of 8,344 outputs

Outputs of similar age

#101

of 341,037 outputs

Outputs of similar age from Nature Biotechnology

of 97 outputs

Altmetric has tracked 23,393,453 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 8,344 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 41.4. 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 341,037 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.

Deep learning enables rapid identification of potent DDR1 kinase inhibitors

About this Attention Score

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Twitter Demographics

Geographical breakdown

Demographic breakdown

Mendeley readers

Geographical breakdown

Demographic breakdown

Attention Score in Context