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A self-attention based message passing neural network for predicting molecular lipophilicity and aqueous solubility

Overview of attention for article published in Journal of Cheminformatics, February 2020
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (86th percentile)

Mentioned by

blogs
1 blog
twitter
15 tweeters

Citations

dimensions_citation
3 Dimensions

Readers on

mendeley
33 Mendeley
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Title
A self-attention based message passing neural network for predicting molecular lipophilicity and aqueous solubility
Published in
Journal of Cheminformatics, February 2020
DOI 10.1186/s13321-020-0414-z
Authors

Bowen Tang, Skyler T. Kramer, Meijuan Fang, Yingkun Qiu, Zhen Wu, Dong Xu

Twitter Demographics

The data shown below were collected from the profiles of 15 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 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 33 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 30%
Student > Ph. D. Student 7 21%
Student > Master 5 15%
Student > Bachelor 3 9%
Student > Doctoral Student 2 6%
Other 4 12%
Unknown 2 6%
Readers by discipline Count As %
Chemistry 15 45%
Computer Science 5 15%
Pharmacology, Toxicology and Pharmaceutical Science 3 9%
Engineering 3 9%
Agricultural and Biological Sciences 1 3%
Other 3 9%
Unknown 3 9%

Attention Score in Context

This research output has an Altmetric Attention Score of 15. 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 27 April 2020.
All research outputs
#1,333,254
of 15,526,302 outputs
Outputs from Journal of Cheminformatics
#131
of 616 outputs
Outputs of similar age
#35,221
of 252,823 outputs
Outputs of similar age from Journal of Cheminformatics
#2
of 3 outputs
Altmetric has tracked 15,526,302 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 616 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.5. This one has done well, scoring higher than 78% 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 252,823 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 86% of its contemporaries.
We're also able to compare this research output to 3 others from the same source and published within six weeks on either side of this one.