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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
  • High Attention Score compared to outputs of the same age (81st percentile)

Mentioned by

twitter
24 tweeters

Citations

dimensions_citation
1 Dimensions

Readers on

mendeley
39 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
Authors

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

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 39 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 39 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 31%
Researcher 10 26%
Student > Master 3 8%
Student > Bachelor 2 5%
Lecturer > Senior Lecturer 1 3%
Other 3 8%
Unknown 8 21%
Readers by discipline Count As %
Chemistry 10 26%
Computer Science 7 18%
Agricultural and Biological Sciences 3 8%
Engineering 2 5%
Pharmacology, Toxicology and Pharmaceutical Science 2 5%
Other 6 15%
Unknown 9 23%

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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
#1,975,624
of 16,578,007 outputs
Outputs from Journal of Cheminformatics
#213
of 645 outputs
Outputs of similar age
#57,751
of 306,090 outputs
Outputs of similar age from Journal of Cheminformatics
#1
of 1 outputs
Altmetric has tracked 16,578,007 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 645 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 gotten more attention than average, scoring higher than 67% 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 306,090 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 81% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them