The data shown below were collected from the profiles of 1,432 X users who shared this research output. Click here to find out more about how the information was compiled.
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| 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
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 255 | 18% |
| United Kingdom | 55 | 4% |
| Russia | 43 | 3% |
| India | 42 | 3% |
| France | 34 | 2% |
| Canada | 31 | 2% |
| Germany | 20 | 1% |
| Spain | 18 | 1% |
| Ukraine | 16 | 1% |
| Other | 207 | 14% |
| Unknown | 711 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1134 | 79% |
| Scientists | 237 | 17% |
| Practitioners (doctors, other healthcare professionals) | 32 | 2% |
| Science communicators (journalists, bloggers, editors) | 27 | 2% |
| Unknown | 2 | <1% |
Mendeley readers
The data shown below were compiled from readership statistics for 1,362 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1362 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 290 | 21% |
| Student > Ph. D. Student | 222 | 16% |
| Student > Master | 121 | 9% |
| Student > Bachelor | 105 | 8% |
| Other | 66 | 5% |
| Other | 147 | 11% |
| Unknown | 411 | 30% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 195 | 14% |
| Chemistry | 186 | 14% |
| Computer Science | 131 | 10% |
| Agricultural and Biological Sciences | 75 | 6% |
| Pharmacology, Toxicology and Pharmaceutical Science | 65 | 5% |
| Other | 246 | 18% |
| Unknown | 464 | 34% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1607. 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 12 October 2024.
All research outputs
#7,453
of 26,783,796 outputs
Outputs from Nature Biotechnology
#11
of 8,838 outputs
Outputs of similar age
#111
of 353,767 outputs
Outputs of similar age from Nature Biotechnology
#1
of 97 outputs
Altmetric has tracked 26,783,796 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 8,838 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 44.9. 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 353,767 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.