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Jmol SMILES and Jmol SMARTS: specifications and applications

Overview of attention for article published in Journal of Cheminformatics, September 2016
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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 (87th percentile)
  • High Attention Score compared to outputs of the same age and source (83rd percentile)

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Title
Jmol SMILES and Jmol SMARTS: specifications and applications
Published in
Journal of Cheminformatics, September 2016
DOI 10.1186/s13321-016-0160-4
Pubmed ID
Authors

Robert M. Hanson

Abstract

SMILES and SMARTS are two well-defined structure matching languages that have gained wide use in cheminformatics. Jmol is a widely used open-source molecular visualization and analysis tool written in Java and implemented in both Java and JavaScript. Over the past 10 years, from 2007 to 2016, work on Jmol has included the development of dialects of SMILES and SMARTS that incorporate novel aspects that allow new and powerful applications. The specifications of "Jmol SMILES" and "Jmol SMARTS" are described. The dialects most closely resemble OpenSMILES and OpenSMARTS. Jmol SMILES is a superset of OpenSMILES, allowing a freer format, including whitespace and comments, the addition of "processing directives" that modify the meaning of certain aspects of SMILES processing such as aromaticity and stereochemistry, a more extensive treatment of stereochemistry, and several minor additions. Jmol SMARTS similarly adds these same modifications to OpenSMARTS, but also adds a number of additional "primitives" and elements of syntax tuned to matching 3D molecular structures and selecting their atoms. The result is an expansion of the capabilities of SMILES and SMARTS primarily for use in 3D molecular analysis, allowing a broader range of matching involving any combination of 3D molecular structures, SMILES strings, and SMARTS patterns. While developed specifically for Jmol, these dialects of SMILES and SMARTS are independent of the Jmol application itself. Jmol SMILES and Jmol SMARTS add value to standard SMILES and SMARTS. Together they have proven exceptionally capable in extracting valuable information from 3D structural models, as demonstrated in Jmol. Capabilities in Jmol enabled by Jmol SMILES and Jmol SMARTS include efficient MMFF94 atom typing, conformational identification, SMILES comparisons without canonicalization, identification of stereochemical relationships, quantitative comparison of 3D structures from different sources (including differences in Kekulization), conformational flexible fitting, and atom mapping used to synchronize interactive displays of 2D structures, 3D structures, and spectral correlations, where data are being drawn from multiple sources.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 56 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
Brazil 1 2%
Unknown 54 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 14 25%
Student > Ph. D. Student 9 16%
Student > Bachelor 8 14%
Student > Master 6 11%
Professor > Associate Professor 3 5%
Other 6 11%
Unknown 10 18%
Readers by discipline Count As %
Chemistry 15 27%
Biochemistry, Genetics and Molecular Biology 7 13%
Pharmacology, Toxicology and Pharmaceutical Science 5 9%
Agricultural and Biological Sciences 3 5%
Computer Science 2 4%
Other 10 18%
Unknown 14 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 14. 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 02 November 2017.
All research outputs
#2,456,243
of 24,143,470 outputs
Outputs from Journal of Cheminformatics
#235
of 891 outputs
Outputs of similar age
#42,447
of 327,584 outputs
Outputs of similar age from Journal of Cheminformatics
#5
of 24 outputs
Altmetric has tracked 24,143,470 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 891 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.7. This one has gotten more attention than average, scoring higher than 73% 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 327,584 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 87% of its contemporaries.
We're also able to compare this research output to 24 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 83% of its contemporaries.