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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models
|
|---|---|
| Published in |
Journal of Cheminformatics, February 2021
|
| DOI | 10.1186/s13321-020-00479-8 |
| Pubmed ID | |
| Authors |
Dejun Jiang, Zhenxing Wu, Chang-Yu Hsieh, Guangyong Chen, Ben Liao, Zhe Wang, Chao Shen, Dongsheng Cao, Jian Wu, Tingjun Hou |
X Demographics
The data shown below were collected from the profiles of 27 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 | 5 | 19% |
| Sweden | 2 | 7% |
| Japan | 2 | 7% |
| Spain | 2 | 7% |
| Netherlands | 1 | 4% |
| Comoros | 1 | 4% |
| Thailand | 1 | 4% |
| Norway | 1 | 4% |
| Unknown | 12 | 44% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 18 | 67% |
| Scientists | 8 | 30% |
| Science communicators (journalists, bloggers, editors) | 1 | 4% |
Mendeley readers
The data shown below were compiled from readership statistics for 395 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 395 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 60 | 15% |
| Student > Ph. D. Student | 54 | 14% |
| Student > Master | 41 | 10% |
| Student > Bachelor | 27 | 7% |
| Other | 20 | 5% |
| Other | 42 | 11% |
| Unknown | 151 | 38% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 56 | 14% |
| Computer Science | 53 | 13% |
| Biochemistry, Genetics and Molecular Biology | 25 | 6% |
| Engineering | 24 | 6% |
| Materials Science | 14 | 4% |
| Other | 57 | 14% |
| Unknown | 166 | 42% |
Attention Score in Context
This research output has an Altmetric Attention Score of 18. 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 19 April 2024.
All research outputs
#2,059,310
of 25,746,891 outputs
Outputs from Journal of Cheminformatics
#159
of 981 outputs
Outputs of similar age
#55,510
of 458,272 outputs
Outputs of similar age from Journal of Cheminformatics
#7
of 28 outputs
Altmetric has tracked 25,746,891 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 981 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.9. This one has done well, scoring higher than 83% 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 458,272 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 87% of its contemporaries.
We're also able to compare this research output to 28 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.