| Title |
An exploration strategy improves the diversity of de novo ligands using deep reinforcement learning: a case for the adenosine A2A receptor
|
|---|---|
| Published in |
Journal of Cheminformatics, May 2019
|
| DOI | 10.1186/s13321-019-0355-6 |
| Pubmed ID | |
| Authors |
Xuhan Liu, Kai Ye, Herman W. T. van Vlijmen, Adriaan P. IJzerman, Gerard J. P. van Westen |
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 % |
|---|---|---|
| Netherlands | 2 | 13% |
| Thailand | 1 | 7% |
| Sweden | 1 | 7% |
| France | 1 | 7% |
| Unknown | 10 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 8 | 53% |
| Scientists | 5 | 33% |
| Science communicators (journalists, bloggers, editors) | 2 | 13% |
Mendeley readers
The data shown below were compiled from readership statistics for 119 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 119 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 22 | 18% |
| Researcher | 17 | 14% |
| Student > Bachelor | 13 | 11% |
| Student > Ph. D. Student | 11 | 9% |
| Other | 5 | 4% |
| Other | 16 | 13% |
| Unknown | 35 | 29% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 20 | 17% |
| Biochemistry, Genetics and Molecular Biology | 13 | 11% |
| Pharmacology, Toxicology and Pharmaceutical Science | 12 | 10% |
| Computer Science | 10 | 8% |
| Medicine and Dentistry | 5 | 4% |
| Other | 17 | 14% |
| Unknown | 42 | 35% |
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 23 September 2019.
All research outputs
#4,799,341
of 25,837,817 outputs
Outputs from Journal of Cheminformatics
#428
of 981 outputs
Outputs of similar age
#89,155
of 366,874 outputs
Outputs of similar age from Journal of Cheminformatics
#8
of 15 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 81st 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 56% 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 366,874 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 75% of its contemporaries.
We're also able to compare this research output to 15 others from the same source and published within six weeks on either side of this one. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.