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Automated reaction database and reaction network analysis: extraction of reaction templates using cheminformatics

Overview of attention for article published in Journal of Cheminformatics, March 2018
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (82nd percentile)
  • Good Attention Score compared to outputs of the same age and source (68th percentile)

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19 X users
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1 Google+ user

Citations

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29 Dimensions

Readers on

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149 Mendeley
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Title
Automated reaction database and reaction network analysis: extraction of reaction templates using cheminformatics
Published in
Journal of Cheminformatics, March 2018
DOI 10.1186/s13321-018-0269-8
Pubmed ID
Authors

Pieter P. Plehiers, Guy B. Marin, Christian V. Stevens, Kevin M. Van Geem

Abstract

Both the automated generation of reaction networks and the automated prediction of synthetic trees require, in one way or another, the definition of possible transformations a molecule can undergo. One way of doing this is by using reaction templates. In view of the expanding amount of known reactions, it has become more and more difficult to envision all possible transformations that could occur in a studied system. Nonetheless, most reaction network generation tools rely on user-defined reaction templates. Not only does this limit the amount of chemistry that can be accounted for in the reaction networks, it also confines the wide-spread use of the tools by a broad public. In retrosynthetic analysis, the quality of the analysis depends on what percentage of the known chemistry is accounted for. Using databases to identify templates is therefore crucial in this respect. For this purpose, an algorithm has been developed to extract reaction templates from various types of chemical databases. Some databases such as the Kyoto Encyclopedia for Genes and Genomes and RMG do not report an atom-atom mapping (AAM) for the reactions. This makes the extraction of a template non-straightforward. If no mapping is available, it is calculated by the Reaction Decoder Tool (RDT). With a correct AAM-either calculated by RDT or specified-the algorithm consistently extracts a correct template for a wide variety of reactions, both elementary and non-elementary. The developed algorithm is a first step towards data-driven generation of synthetic trees or reaction networks, and a greater accessibility for non-expert users.

X Demographics

X Demographics

The data shown below were collected from the profiles of 19 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 149 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 149 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 36 24%
Student > Ph. D. Student 27 18%
Student > Master 18 12%
Student > Bachelor 15 10%
Other 6 4%
Other 14 9%
Unknown 33 22%
Readers by discipline Count As %
Chemistry 44 30%
Chemical Engineering 15 10%
Agricultural and Biological Sciences 10 7%
Biochemistry, Genetics and Molecular Biology 10 7%
Computer Science 8 5%
Other 21 14%
Unknown 41 28%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 12. 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 March 2019.
All research outputs
#3,030,248
of 25,335,657 outputs
Outputs from Journal of Cheminformatics
#276
of 955 outputs
Outputs of similar age
#59,645
of 339,039 outputs
Outputs of similar age from Journal of Cheminformatics
#7
of 19 outputs
Altmetric has tracked 25,335,657 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 955 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.1. This one has gotten more attention than average, scoring higher than 71% 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 339,039 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 82% 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 68% of its contemporaries.