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Smiles2Monomers: a link between chemical and biological structures for polymers

Overview of attention for article published in Journal of Cheminformatics, December 2015
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Title
Smiles2Monomers: a link between chemical and biological structures for polymers
Published in
Journal of Cheminformatics, December 2015
DOI 10.1186/s13321-015-0111-5
Pubmed ID
Authors

Yoann Dufresne, Laurent Noé, Valérie Leclère, Maude Pupin

Abstract

The monomeric composition of polymers is powerful for structure comparison and synthetic biology, among others. Many databases give access to the atomic structure of compounds but the monomeric structure of polymers is often lacking. We have designed a smart algorithm, implemented in the tool Smiles2Monomers (s2m), to infer efficiently and accurately the monomeric structure of a polymer from its chemical structure. Our strategy is divided into two steps: first, monomers are mapped on the atomic structure by an efficient subgraph-isomorphism algorithm ; second, the best tiling is computed so that non-overlapping monomers cover all the structure of the target polymer. The mapping is based on a Markovian index built by a dynamic programming algorithm. The index enables s2m to search quickly all the given monomers on a target polymer. After, a greedy algorithm combines the mapped monomers into a consistent monomeric structure. Finally, a local branch and cut algorithm refines the structure. We tested this method on two manually annotated databases of polymers and reconstructed the structures de novo with a sensitivity over 90 %. The average computation time per polymer is 2 s. s2m automatically creates de novo monomeric annotations for polymers, efficiently in terms of time computation and sensitivity. s2m allowed us to detect annotation errors in the tested databases and to easily find the accurate structures. So, s2m could be integrated into the curation process of databases of small compounds to verify the current entries and accelerate the annotation of new polymers. The full method can be downloaded or accessed via a website for peptide-like polymers at http://bioinfo.lifl.fr/norine/smiles2monomers.jsp.Graphical abstract:.

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X Demographics

The data shown below were collected from the profiles of 7 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 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 4%
Brazil 1 4%
Unknown 23 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 24%
Professor > Associate Professor 4 16%
Researcher 4 16%
Student > Master 3 12%
Student > Bachelor 2 8%
Other 3 12%
Unknown 3 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 5 20%
Chemistry 5 20%
Engineering 4 16%
Biochemistry, Genetics and Molecular Biology 3 12%
Computer Science 3 12%
Other 3 12%
Unknown 2 8%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 14 January 2016.
All research outputs
#6,002,660
of 22,836,570 outputs
Outputs from Journal of Cheminformatics
#497
of 834 outputs
Outputs of similar age
#95,353
of 392,772 outputs
Outputs of similar age from Journal of Cheminformatics
#9
of 18 outputs
Altmetric has tracked 22,836,570 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 834 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.9. This one is in the 40th percentile – i.e., 40% of its peers scored the same or lower than it.
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 392,772 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 75% 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 has gotten more attention than average, scoring higher than 50% of its contemporaries.