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Using SMILES strings for the description of chemical connectivity in the Crystallography Open Database

Overview of attention for article published in Journal of Cheminformatics, May 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 (89th percentile)
  • High Attention Score compared to outputs of the same age and source (92nd percentile)

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3 blogs
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Title
Using SMILES strings for the description of chemical connectivity in the Crystallography Open Database
Published in
Journal of Cheminformatics, May 2018
DOI 10.1186/s13321-018-0279-6
Pubmed ID
Authors

Miguel Quirós, Saulius Gražulis, Saulė Girdzijauskaitė, Andrius Merkys, Antanas Vaitkus

Abstract

Computer descriptions of chemical molecular connectivity are necessary for searching chemical databases and for predicting chemical properties from molecular structure. In this article, the ongoing work to describe the chemical connectivity of entries contained in the Crystallography Open Database (COD) in SMILES format is reported. This collection of SMILES is publicly available for chemical (substructure) search or for any other purpose on an open-access basis, as is the COD itself. The conventions that have been followed for the representation of compounds that do not fit into the valence bond theory are outlined for the most frequently found cases. The procedure for getting the SMILES out of the CIF files starts with checking whether the atoms in the asymmetric unit are a chemically acceptable image of the compound. When they are not (molecule in a symmetry element, disorder, polymeric species,etc.), the previously published cif_molecule program is used to get such image in many cases. The program package Open Babel is then applied to get SMILES strings from the CIF files (either those directly taken from the COD or those produced by cif_molecule when applicable). The results are then checked and/or fixed by a human editor, in a computer-aided task that at present still consumes a great deal of human time. Even if the procedure still needs to be improved to make it more automatic (and hence faster), it has already yielded more than 160,000 curated chemical structures and the purpose of this article is to announce the existence of this work to the chemical community as well as to spread the use of its results.

X Demographics

X Demographics

The data shown below were collected from the profiles of 8 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 241 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 241 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 41 17%
Student > Master 35 15%
Researcher 31 13%
Student > Bachelor 16 7%
Student > Doctoral Student 12 5%
Other 31 13%
Unknown 75 31%
Readers by discipline Count As %
Chemistry 46 19%
Materials Science 26 11%
Engineering 19 8%
Physics and Astronomy 13 5%
Chemical Engineering 9 4%
Other 40 17%
Unknown 88 37%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 23. 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 01 September 2022.
All research outputs
#1,589,548
of 24,903,209 outputs
Outputs from Journal of Cheminformatics
#109
of 934 outputs
Outputs of similar age
#33,695
of 335,427 outputs
Outputs of similar age from Journal of Cheminformatics
#2
of 14 outputs
Altmetric has tracked 24,903,209 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 934 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.2. This one has done well, scoring higher than 88% 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 335,427 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 89% of its contemporaries.
We're also able to compare this research output to 14 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 92% of its contemporaries.