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Template-based combinatorial enumeration of virtual compound libraries for lipids

Overview of attention for article published in Journal of Cheminformatics, September 2012
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Title
Template-based combinatorial enumeration of virtual compound libraries for lipids
Published in
Journal of Cheminformatics, September 2012
DOI 10.1186/1758-2946-4-23
Pubmed ID
Authors

Manish Sud, Eoin Fahy, Shankar Subramaniam

Abstract

A variety of software packages are available for the combinatorial enumeration of virtual libraries for small molecules, starting from specifications of core scaffolds with attachments points and lists of R-groups as SMILES or SD files. Although SD files include atomic coordinates for core scaffolds and R-groups, it is not possible to control 2-dimensional (2D) layout of the enumerated structures generated for virtual compound libraries because different packages generate different 2D representations for the same structure. We have developed a software package called LipidMapsTools for the template-based combinatorial enumeration of virtual compound libraries for lipids. Virtual libraries are enumerated for the specified lipid abbreviations using matching lists of pre-defined templates and chain abbreviations, instead of core scaffolds and lists of R-groups provided by the user. 2D structures of the enumerated lipids are drawn in a specific and consistent fashion adhering to the framework for representing lipid structures proposed by the LIPID MAPS consortium. LipidMapsTools is lightweight, relatively fast and contains no external dependencies. It is an open source package and freely available under the terms of the modified BSD license.

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X Demographics

The data shown below were collected from the profile of 1 X user 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 38 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 1 3%
United Kingdom 1 3%
Canada 1 3%
Sri Lanka 1 3%
Spain 1 3%
Japan 1 3%
United States 1 3%
Unknown 31 82%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 26%
Researcher 9 24%
Lecturer > Senior Lecturer 3 8%
Student > Master 3 8%
Lecturer 2 5%
Other 7 18%
Unknown 4 11%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 8 21%
Chemistry 7 18%
Medicine and Dentistry 5 13%
Agricultural and Biological Sciences 4 11%
Pharmacology, Toxicology and Pharmaceutical Science 3 8%
Other 5 13%
Unknown 6 16%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 25 September 2012.
All research outputs
#18,316,001
of 22,679,690 outputs
Outputs from Journal of Cheminformatics
#792
of 826 outputs
Outputs of similar age
#130,333
of 171,685 outputs
Outputs of similar age from Journal of Cheminformatics
#11
of 13 outputs
Altmetric has tracked 22,679,690 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 826 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.0. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 13 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.