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PyMine: a PyMOL plugin to integrate and visualize data for drug discovery

Overview of attention for article published in BMC Research Notes, October 2015
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (63rd percentile)
  • Good Attention Score compared to outputs of the same age and source (76th percentile)

Mentioned by

twitter
4 tweeters

Citations

dimensions_citation
20 Dimensions

Readers on

mendeley
30 Mendeley

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Summary Twitter Dimensions citations
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Title
PyMine: a PyMOL plugin to integrate and visualize data for drug discovery
Published in
BMC Research Notes, October 2015
DOI 10.1186/s13104-015-1483-3
Pubmed ID
26429562
Authors

Rajan Chaudhari, Zhijun Li

Abstract

Tremendous amount of chemical and biological data are being generated by various high-throughput biotechnologies that could facilitate modern drug discovery. However, lack of integration makes it very challenging for individual scientists to access and understand all the data related to a specific protein of interest. To overcome this challenge, we developed PyMine, a PyMOL plugin that retrieves chemical, structural, pathway and other related biological data of a receptor and small molecules from a variety of high-quality databases and presents them in a graphic and uniformed way. Developed as an interactive and user-friendly tool, PyMine can be used as a central data-hub for users to access and visualize multiple types of data and to generate new ideas intuitively for structure-based molecule design.

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Twitter Demographics

The data shown below were collected from the profiles of 4 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 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Indonesia 1 3%
Unknown 29 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 23%
Researcher 4 13%
Student > Master 3 10%
Student > Bachelor 2 7%
Lecturer 2 7%
Other 4 13%
Unknown 8 27%
Readers by discipline Count As %
Agricultural and Biological Sciences 8 27%
Chemistry 6 20%
Biochemistry, Genetics and Molecular Biology 3 10%
Pharmacology, Toxicology and Pharmaceutical Science 2 7%
Computer Science 1 3%
Other 2 7%
Unknown 8 27%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 02 October 2015.
All research outputs
#5,814,939
of 11,214,196 outputs
Outputs from BMC Research Notes
#831
of 2,458 outputs
Outputs of similar age
#87,979
of 244,119 outputs
Outputs of similar age from BMC Research Notes
#39
of 166 outputs
Altmetric has tracked 11,214,196 research outputs across all sources so far. This one is in the 47th percentile – i.e., 47% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,458 research outputs from this source. They receive a mean Attention Score of 4.6. This one has gotten more attention than average, scoring higher than 65% 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 244,119 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 63% of its contemporaries.
We're also able to compare this research output to 166 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.

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