You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output.
Click here to find out more.
X Demographics
Mendeley readers
Attention Score in Context
| Title |
Multiobjective de novo drug design with recurrent neural networks and nondominated sorting
|
|---|---|
| Published in |
Journal of Cheminformatics, February 2020
|
| DOI | 10.1186/s13321-020-00419-6 |
| Pubmed ID | |
| Authors |
Jacob Yasonik |
X Demographics
The data shown below were collected from the profiles of 17 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 | 4 | 24% |
| Korea, Democratic People's Republic of | 1 | 6% |
| United States | 1 | 6% |
| Australia | 1 | 6% |
| Sweden | 1 | 6% |
| Unknown | 9 | 53% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 13 | 76% |
| Scientists | 2 | 12% |
| Practitioners (doctors, other healthcare professionals) | 1 | 6% |
| Science communicators (journalists, bloggers, editors) | 1 | 6% |
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 | 11 | 14% |
| Student > Bachelor | 9 | 11% |
| Student > Ph. D. Student | 8 | 10% |
| Student > Master | 8 | 10% |
| Professor > Associate Professor | 6 | 8% |
| Other | 13 | 16% |
| Unknown | 24 | 30% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 18 | 23% |
| Computer Science | 10 | 13% |
| Biochemistry, Genetics and Molecular Biology | 8 | 10% |
| Pharmacology, Toxicology and Pharmaceutical Science | 4 | 5% |
| Agricultural and Biological Sciences | 3 | 4% |
| Other | 11 | 14% |
| Unknown | 25 | 32% |
Attention Score in Context
This research output has an Altmetric Attention Score of 9. 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 05 March 2020.
All research outputs
#4,183,894
of 25,837,817 outputs
Outputs from Journal of Cheminformatics
#354
of 981 outputs
Outputs of similar age
#83,428
of 384,973 outputs
Outputs of similar age from Journal of Cheminformatics
#13
of 19 outputs
Altmetric has tracked 25,837,817 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 981 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.0. This one has gotten more attention than average, scoring higher than 63% 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 384,973 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 78% of its contemporaries.
We're also able to compare this research output to 19 others from the same source and published within six weeks on either side of this one. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.