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Scaffold analysis of PubChem database as background for hierarchical scaffold-based visualization

Overview of attention for article published in Journal of Cheminformatics, December 2016
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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 (87th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (57th percentile)

Mentioned by

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1 blog
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10 X users
googleplus
1 Google+ user

Citations

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12 Dimensions

Readers on

mendeley
53 Mendeley
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Title
Scaffold analysis of PubChem database as background for hierarchical scaffold-based visualization
Published in
Journal of Cheminformatics, December 2016
DOI 10.1186/s13321-016-0186-7
Pubmed ID
Authors

Jakub Velkoborsky, David Hoksza

Abstract

Visualization of large molecular datasets is a challenging yet important topic utilised in diverse fields of chemistry ranging from material engineering to drug design. Especially in drug design, modern methods of high-throughput screening generate large amounts of molecular data that call for methods enabling their analysis. One such method is classification of compounds based on their molecular scaffolds, a concept widely used by medicinal chemists to group molecules of similar properties. This classification can then be utilized for intuitive visualization of compounds. In this paper, we propose a scaffold hierarchy as a result of large-scale analysis of the PubChem Compound database. The analysis not only provided insights into scaffold diversity of the PubChem Compound database, but also enables scaffold-based hierarchical visualization of user compound data sets on the background of empirical chemical space, as defined by the PubChem data, or on the background of any other user-defined data set. The visualization is performed by a web based client-server application called Scaffvis. It provides an interactive zoomable tree map visualization of data sets up to hundreds of thousands molecules. Scaffvis is free to use and its source codes have been published under an open source license.Graphical abstract.

X Demographics

X Demographics

The data shown below were collected from the profiles of 10 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 53 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
Unknown 52 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 17 32%
Student > Ph. D. Student 9 17%
Other 6 11%
Student > Bachelor 3 6%
Professor > Associate Professor 3 6%
Other 6 11%
Unknown 9 17%
Readers by discipline Count As %
Chemistry 13 25%
Biochemistry, Genetics and Molecular Biology 6 11%
Agricultural and Biological Sciences 6 11%
Pharmacology, Toxicology and Pharmaceutical Science 5 9%
Environmental Science 3 6%
Other 7 13%
Unknown 13 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 13. 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 November 2019.
All research outputs
#2,625,486
of 24,903,209 outputs
Outputs from Journal of Cheminformatics
#233
of 934 outputs
Outputs of similar age
#51,860
of 432,238 outputs
Outputs of similar age from Journal of Cheminformatics
#9
of 19 outputs
Altmetric has tracked 24,903,209 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 934 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 done well, scoring higher than 75% 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 432,238 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 87% of its contemporaries.
We're also able to compare this research output to 19 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 57% of its contemporaries.