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Machine learning approaches to optimize small-molecule inhibitors for RNA targeting

Overview of attention for article published in Journal of Cheminformatics, February 2022
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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 (92nd percentile)
  • Good Attention Score compared to outputs of the same age and source (76th percentile)

Mentioned by

twitter
38 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
3 Dimensions

Readers on

mendeley
25 Mendeley
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Title
Machine learning approaches to optimize small-molecule inhibitors for RNA targeting
Published in
Journal of Cheminformatics, February 2022
DOI 10.1186/s13321-022-00583-x
Pubmed ID
Authors

Hadar Grimberg, Vinay S. Tiwari, Benjamin Tam, Lihi Gur-Arie, Daniela Gingold, Lea Polachek, Barak Akabayov

Twitter Demographics

The data shown below were collected from the profiles of 38 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 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 25 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 16%
Student > Ph. D. Student 4 16%
Unspecified 3 12%
Student > Master 3 12%
Student > Bachelor 1 4%
Other 1 4%
Unknown 9 36%
Readers by discipline Count As %
Unspecified 4 16%
Chemistry 3 12%
Computer Science 3 12%
Biochemistry, Genetics and Molecular Biology 2 8%
Pharmacology, Toxicology and Pharmaceutical Science 1 4%
Other 2 8%
Unknown 10 40%

Attention Score in Context

This research output has an Altmetric Attention Score of 23. 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 17 March 2022.
All research outputs
#1,412,580
of 23,020,670 outputs
Outputs from Journal of Cheminformatics
#109
of 841 outputs
Outputs of similar age
#35,998
of 504,137 outputs
Outputs of similar age from Journal of Cheminformatics
#4
of 17 outputs
Altmetric has tracked 23,020,670 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 841 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.0. This one has done well, scoring higher than 87% 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 504,137 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 17 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 76% of its contemporaries.