| Title |
Molecular de-novo design through deep reinforcement learning
|
|---|---|
| Published in |
Journal of Cheminformatics, September 2017
|
| DOI | 10.1186/s13321-017-0235-x |
| Pubmed ID | |
| Authors |
Marcus Olivecrona, Thomas Blaschke, Ola Engkvist, Hongming Chen |
X Demographics
The data shown below were collected from the profiles of 24 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 4 | 17% |
| Japan | 3 | 13% |
| United Kingdom | 2 | 8% |
| India | 1 | 4% |
| Comoros | 1 | 4% |
| Netherlands | 1 | 4% |
| Sweden | 1 | 4% |
| Russia | 1 | 4% |
| Unknown | 10 | 42% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 15 | 63% |
| Scientists | 8 | 33% |
| Science communicators (journalists, bloggers, editors) | 1 | 4% |
Mendeley readers
The data shown below were compiled from readership statistics for 928 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Japan | 2 | <1% |
| United States | 1 | <1% |
| Unknown | 925 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 205 | 22% |
| Student > Ph. D. Student | 159 | 17% |
| Student > Master | 128 | 14% |
| Student > Bachelor | 78 | 8% |
| Other | 33 | 4% |
| Other | 89 | 10% |
| Unknown | 236 | 25% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 174 | 19% |
| Computer Science | 158 | 17% |
| Biochemistry, Genetics and Molecular Biology | 62 | 7% |
| Engineering | 46 | 5% |
| Pharmacology, Toxicology and Pharmaceutical Science | 45 | 5% |
| Other | 160 | 17% |
| Unknown | 283 | 30% |
Attention Score in Context
This research output has an Altmetric Attention Score of 52. 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 22 April 2024.
All research outputs
#827,330
of 25,761,363 outputs
Outputs from Journal of Cheminformatics
#17
of 981 outputs
Outputs of similar age
#16,798
of 324,705 outputs
Outputs of similar age from Journal of Cheminformatics
#1
of 11 outputs
Altmetric has tracked 25,761,363 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% 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 done particularly well, scoring higher than 98% 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 324,705 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 94% of its contemporaries.
We're also able to compare this research output to 11 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 90% of its contemporaries.