↓ Skip to main content

Randomized SMILES strings improve the quality of molecular generative models

Overview of attention for article published in Journal of Cheminformatics, November 2019
Altmetric Badge

About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (92nd percentile)
  • High Attention Score compared to outputs of the same age and source (92nd percentile)

Mentioned by

blogs
3 blogs
twitter
24 tweeters

Citations

dimensions_citation
97 Dimensions

Readers on

mendeley
195 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Randomized SMILES strings improve the quality of molecular generative models
Published in
Journal of Cheminformatics, November 2019
DOI 10.1186/s13321-019-0393-0
Pubmed ID
Authors

Josep Arús-Pous, Simon Viet Johansson, Oleksii Prykhodko, Esben Jannik Bjerrum, Christian Tyrchan, Jean-Louis Reymond, Hongming Chen, Ola Engkvist

Twitter Demographics

The data shown below were collected from the profiles of 24 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 195 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 195 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 46 24%
Student > Ph. D. Student 33 17%
Student > Master 25 13%
Student > Bachelor 20 10%
Other 9 5%
Other 13 7%
Unknown 49 25%
Readers by discipline Count As %
Chemistry 47 24%
Computer Science 28 14%
Biochemistry, Genetics and Molecular Biology 17 9%
Engineering 8 4%
Pharmacology, Toxicology and Pharmaceutical Science 8 4%
Other 26 13%
Unknown 61 31%

Attention Score in Context

This research output has an Altmetric Attention Score of 31. 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 14 September 2022.
All research outputs
#1,031,818
of 22,078,848 outputs
Outputs from Journal of Cheminformatics
#55
of 808 outputs
Outputs of similar age
#31,609
of 439,719 outputs
Outputs of similar age from Journal of Cheminformatics
#6
of 78 outputs
Altmetric has tracked 22,078,848 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 808 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.1. This one has done particularly well, scoring higher than 93% 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 439,719 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 92% of its contemporaries.
We're also able to compare this research output to 78 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 92% of its contemporaries.