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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Chemical space exploration based on recurrent neural networks: applications in discovering kinase inhibitors
|
|---|---|
| Published in |
Journal of Cheminformatics, June 2020
|
| DOI | 10.1186/s13321-020-00446-3 |
| Pubmed ID | |
| Authors |
Xuanyi Li, Yinqiu Xu, Hequan Yao, Kejiang Lin |
X Demographics
The data shown below were collected from the profiles of 15 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Japan | 1 | 7% |
| Mexico | 1 | 7% |
| France | 1 | 7% |
| Unknown | 12 | 80% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 11 | 73% |
| Scientists | 3 | 20% |
| Science communicators (journalists, bloggers, editors) | 1 | 7% |
Mendeley readers
The data shown below were compiled from readership statistics for 79 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 79 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 14 | 18% |
| Student > Ph. D. Student | 9 | 11% |
| Student > Bachelor | 8 | 10% |
| Student > Master | 6 | 8% |
| Student > Doctoral Student | 3 | 4% |
| Other | 8 | 10% |
| Unknown | 31 | 39% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 13 | 16% |
| Biochemistry, Genetics and Molecular Biology | 7 | 9% |
| Pharmacology, Toxicology and Pharmaceutical Science | 5 | 6% |
| Engineering | 5 | 6% |
| Social Sciences | 3 | 4% |
| Other | 11 | 14% |
| Unknown | 35 | 44% |
Attention Score in Context
This research output has an Altmetric Attention Score of 8. 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 14 June 2020.
All research outputs
#4,497,433
of 24,143,470 outputs
Outputs from Journal of Cheminformatics
#427
of 891 outputs
Outputs of similar age
#106,248
of 402,204 outputs
Outputs of similar age from Journal of Cheminformatics
#9
of 17 outputs
Altmetric has tracked 24,143,470 research outputs across all sources so far. Compared to these this one has done well and is in the 81st percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 891 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.7. This one has gotten more attention than average, scoring higher than 51% 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 402,204 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.
We're also able to compare this research output to 17 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 52% of its contemporaries.