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Finding the K best synthesis plans

Overview of attention for article published in Journal of Cheminformatics, April 2018
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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 (80th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
16 tweeters
peer_reviews
1 peer review site

Citations

dimensions_citation
9 Dimensions

Readers on

mendeley
50 Mendeley
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Title
Finding the K best synthesis plans
Published in
Journal of Cheminformatics, April 2018
DOI 10.1186/s13321-018-0273-z
Pubmed ID
Authors

Rolf Fagerberg, Christoph Flamm, Rojin Kianian, Daniel Merkle, Peter F. Stadler

Abstract

In synthesis planning, the goal is to synthesize a target molecule from available starting materials, possibly optimizing costs such as price or environmental impact of the process. Current algorithmic approaches to synthesis planning are usually based on selecting a bond set and finding a single good plan among those induced by it. We demonstrate that synthesis planning can be phrased as a combinatorial optimization problem on hypergraphs by modeling individual synthesis plans as directed hyperpaths embedded in a hypergraph of reactions (HoR) representing the chemistry of interest. As a consequence, a polynomial time algorithm to find the K shortest hyperpaths can be used to compute the K best synthesis plans for a given target molecule. Having K good plans to choose from has many benefits: it makes the synthesis planning process much more robust when in later stages adding further chemical detail, it allows one to combine several notions of cost, and it provides a way to deal with imprecise yield estimates. A bond set gives rise to a HoR in a natural way. However, our modeling is not restricted to bond set based approaches-any set of known reactions and starting materials can be used to define a HoR. We also discuss classical quality measures for synthesis plans, such as overall yield and convergency, and demonstrate that convergency has a built-in inconsistency which could render its use in synthesis planning questionable. Decalin is used as an illustrative example of the use and implications of our results.

Twitter Demographics

The data shown below were collected from the profiles of 16 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 50 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 50 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 16 32%
Student > Ph. D. Student 10 20%
Student > Bachelor 8 16%
Student > Master 4 8%
Unspecified 2 4%
Other 3 6%
Unknown 7 14%
Readers by discipline Count As %
Chemistry 15 30%
Engineering 10 20%
Computer Science 4 8%
Biochemistry, Genetics and Molecular Biology 3 6%
Agricultural and Biological Sciences 2 4%
Other 7 14%
Unknown 9 18%

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 27 May 2018.
All research outputs
#3,098,627
of 23,041,514 outputs
Outputs from Journal of Cheminformatics
#320
of 842 outputs
Outputs of similar age
#65,781
of 329,678 outputs
Outputs of similar age from Journal of Cheminformatics
#10
of 18 outputs
Altmetric has tracked 23,041,514 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 842 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.9. This one has gotten more attention than average, scoring higher than 61% 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 329,678 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 80% of its contemporaries.
We're also able to compare this research output to 18 others from the same source and published within six weeks on either side of this one. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.