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DeepGraphMolGen, a multi-objective, computational strategy for generating molecules with desirable properties: a graph convolution and reinforcement learning approach

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

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (77th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
20 X users

Citations

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45 Dimensions

Readers on

mendeley
123 Mendeley
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Title
DeepGraphMolGen, a multi-objective, computational strategy for generating molecules with desirable properties: a graph convolution and reinforcement learning approach
Published in
Journal of Cheminformatics, September 2020
DOI 10.1186/s13321-020-00454-3
Pubmed ID
Authors

Yash Khemchandani, Stephen O’Hagan, Soumitra Samanta, Neil Swainston, Timothy J. Roberts, Danushka Bollegala, Douglas B. Kell

X Demographics

X Demographics

The data shown below were collected from the profiles of 20 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 123 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 123 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 28 23%
Student > Ph. D. Student 19 15%
Student > Master 12 10%
Other 5 4%
Student > Bachelor 4 3%
Other 14 11%
Unknown 41 33%
Readers by discipline Count As %
Chemistry 18 15%
Computer Science 14 11%
Biochemistry, Genetics and Molecular Biology 8 7%
Agricultural and Biological Sciences 7 6%
Medicine and Dentistry 6 5%
Other 27 22%
Unknown 43 35%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 11 October 2020.
All research outputs
#3,714,997
of 25,163,238 outputs
Outputs from Journal of Cheminformatics
#339
of 946 outputs
Outputs of similar age
#89,726
of 406,751 outputs
Outputs of similar age from Journal of Cheminformatics
#12
of 17 outputs
Altmetric has tracked 25,163,238 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 946 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.2. This one has gotten more attention than average, scoring higher than 64% 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 406,751 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 77% 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 is in the 35th percentile – i.e., 35% of its contemporaries scored the same or lower than it.