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Molecular structures enumeration and virtual screening in the chemical space with RetroPath2.0

Overview of attention for article published in Journal of Cheminformatics, December 2017
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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)
  • Above-average Attention Score compared to outputs of the same age and source (63rd percentile)

Mentioned by

twitter
15 tweeters

Citations

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7 Dimensions

Readers on

mendeley
53 Mendeley
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Title
Molecular structures enumeration and virtual screening in the chemical space with RetroPath2.0
Published in
Journal of Cheminformatics, December 2017
DOI 10.1186/s13321-017-0252-9
Pubmed ID
Authors

Mathilde Koch, Thomas Duigou, Pablo Carbonell, Jean-Loup Faulon

Abstract

Network generation tools coupled with chemical reaction rules have been mainly developed for synthesis planning and more recently for metabolic engineering. Using the same core algorithm, these tools apply a set of rules to a source set of compounds, stopping when a sink set of compounds has been produced. When using the appropriate sink, source and rules, this core algorithm can be used for a variety of applications beyond those it has been developed for. Here, we showcase the use of the open source workflow RetroPath2.0. First, we mathematically prove that we can generate all structural isomers of a molecule using a reduced set of reaction rules. We then use this enumeration strategy to screen the chemical space around a set of monomers and predict their glass transition temperatures, as well as around aminoglycosides to search structures maximizing antibacterial activity. We also perform a screening around aminoglycosides with enzymatic reaction rules to ensure biosynthetic accessibility. We finally use our workflow on an E. coli model to complete E. coli metabolome, with novel molecules generated using promiscuous enzymatic reaction rules. These novel molecules are searched on the MS spectra of an E. coli cell lysate interfacing our workflow with OpenMS through the KNIME Analytics Platform. We provide an easy to use and modify, modular, and open-source workflow. We demonstrate its versatility through a variety of use cases including molecular structure enumeration, virtual screening in the chemical space, and metabolome completion. Because it is open source and freely available on MyExperiment.org, workflow community contributions should likely expand further the features of the tool, even beyond the use cases presented in the paper.

Twitter Demographics

The data shown below were collected from the profiles of 15 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 53 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 53 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 16 30%
Student > Ph. D. Student 9 17%
Student > Master 7 13%
Student > Bachelor 4 8%
Professor 3 6%
Other 4 8%
Unknown 10 19%
Readers by discipline Count As %
Chemistry 11 21%
Agricultural and Biological Sciences 10 19%
Biochemistry, Genetics and Molecular Biology 6 11%
Engineering 4 8%
Computer Science 3 6%
Other 6 11%
Unknown 13 25%

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 29 June 2018.
All research outputs
#1,839,810
of 13,157,264 outputs
Outputs from Journal of Cheminformatics
#196
of 530 outputs
Outputs of similar age
#72,245
of 384,070 outputs
Outputs of similar age from Journal of Cheminformatics
#23
of 66 outputs
Altmetric has tracked 13,157,264 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 530 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.2. This one has gotten more attention than average, scoring higher than 62% 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 384,070 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 66 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 63% of its contemporaries.