| Title |
Molecular generative model based on conditional variational autoencoder for de novo molecular design
|
|---|---|
| Published in |
Journal of Cheminformatics, July 2018
|
| DOI | 10.1186/s13321-018-0286-7 |
| Pubmed ID | |
| Authors |
Jaechang Lim, Seongok Ryu, Jin Woo Kim, Woo Youn Kim |
X Demographics
The data shown below were collected from the profiles of 10 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| France | 1 | 10% |
| Japan | 1 | 10% |
| Israel | 1 | 10% |
| Unknown | 7 | 70% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 5 | 50% |
| Members of the public | 4 | 40% |
| Science communicators (journalists, bloggers, editors) | 1 | 10% |
Mendeley readers
The data shown below were compiled from readership statistics for 369 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 369 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 79 | 21% |
| Researcher | 64 | 17% |
| Student > Master | 43 | 12% |
| Student > Bachelor | 33 | 9% |
| Other | 19 | 5% |
| Other | 32 | 9% |
| Unknown | 99 | 27% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 69 | 19% |
| Computer Science | 54 | 15% |
| Biochemistry, Genetics and Molecular Biology | 32 | 9% |
| Engineering | 26 | 7% |
| Chemical Engineering | 15 | 4% |
| Other | 57 | 15% |
| Unknown | 116 | 31% |
Attention Score in Context
This research output has an Altmetric Attention Score of 27. 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 November 2023.
All research outputs
#1,475,797
of 25,837,817 outputs
Outputs from Journal of Cheminformatics
#81
of 981 outputs
Outputs of similar age
#30,196
of 342,084 outputs
Outputs of similar age from Journal of Cheminformatics
#2
of 15 outputs
Altmetric has tracked 25,837,817 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd 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 a lot more attention than average, with a mean Attention Score of 10.0. This one has done particularly well, scoring higher than 91% 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 342,084 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 90% 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 has done well, scoring higher than 86% of its contemporaries.