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X Demographics
Mendeley readers
Attention Score in Context
| Title |
MERMAID: an open source automated hit-to-lead method based on deep reinforcement learning
|
|---|---|
| Published in |
Journal of Cheminformatics, November 2021
|
| DOI | 10.1186/s13321-021-00572-6 |
| Pubmed ID | |
| Authors |
Daiki Erikawa, Nobuaki Yasuo, Masakazu Sekijima |
X Demographics
The data shown below were collected from the profiles of 13 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 | 31% |
| Sweden | 1 | 8% |
| United States | 1 | 8% |
| Unknown | 7 | 54% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 11 | 85% |
| Science communicators (journalists, bloggers, editors) | 1 | 8% |
| Scientists | 1 | 8% |
Mendeley readers
The data shown below were compiled from readership statistics for 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 35 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 7 | 20% |
| Student > Master | 5 | 14% |
| Other | 3 | 9% |
| Student > Ph. D. Student | 3 | 9% |
| Professor > Associate Professor | 3 | 9% |
| Other | 4 | 11% |
| Unknown | 10 | 29% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 6 | 17% |
| Biochemistry, Genetics and Molecular Biology | 5 | 14% |
| Chemistry | 5 | 14% |
| Agricultural and Biological Sciences | 2 | 6% |
| Pharmacology, Toxicology and Pharmaceutical Science | 1 | 3% |
| Other | 4 | 11% |
| Unknown | 12 | 34% |
Attention Score in Context
This research output has an Altmetric Attention Score of 21. 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 09 December 2022.
All research outputs
#1,755,401
of 24,972,914 outputs
Outputs from Journal of Cheminformatics
#136
of 936 outputs
Outputs of similar age
#41,931
of 515,905 outputs
Outputs of similar age from Journal of Cheminformatics
#2
of 22 outputs
Altmetric has tracked 24,972,914 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 936 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.3. This one has done well, scoring higher than 85% 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 515,905 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 91% of its contemporaries.
We're also able to compare this research output to 22 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 95% of its contemporaries.