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ClassyFire: automated chemical classification with a comprehensive, computable taxonomy

Overview of attention for article published in Journal of Cheminformatics, November 2016
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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 (92nd percentile)
  • High Attention Score compared to outputs of the same age and source (91st percentile)

Mentioned by

blogs
2 blogs
twitter
23 X users
wikipedia
1 Wikipedia page

Citations

dimensions_citation
851 Dimensions

Readers on

mendeley
662 Mendeley
citeulike
5 CiteULike
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Title
ClassyFire: automated chemical classification with a comprehensive, computable taxonomy
Published in
Journal of Cheminformatics, November 2016
DOI 10.1186/s13321-016-0174-y
Pubmed ID
Authors

Yannick Djoumbou Feunang, Roman Eisner, Craig Knox, Leonid Chepelev, Janna Hastings, Gareth Owen, Eoin Fahy, Christoph Steinbeck, Shankar Subramanian, Evan Bolton, Russell Greiner, David S. Wishart

Abstract

Scientists have long been driven by the desire to describe, organize, classify, and compare objects using taxonomies and/or ontologies. In contrast to biology, geology, and many other scientific disciplines, the world of chemistry still lacks a standardized chemical ontology or taxonomy. Several attempts at chemical classification have been made; but they have mostly been limited to either manual, or semi-automated proof-of-principle applications. This is regrettable as comprehensive chemical classification and description tools could not only improve our understanding of chemistry but also improve the linkage between chemistry and many other fields. For instance, the chemical classification of a compound could help predict its metabolic fate in humans, its druggability or potential hazards associated with it, among others. However, the sheer number (tens of millions of compounds) and complexity of chemical structures is such that any manual classification effort would prove to be near impossible. We have developed a comprehensive, flexible, and computable, purely structure-based chemical taxonomy (ChemOnt), along with a computer program (ClassyFire) that uses only chemical structures and structural features to automatically assign all known chemical compounds to a taxonomy consisting of >4800 different categories. This new chemical taxonomy consists of up to 11 different levels (Kingdom, SuperClass, Class, SubClass, etc.) with each of the categories defined by unambiguous, computable structural rules. Furthermore each category is named using a consensus-based nomenclature and described (in English) based on the characteristic common structural properties of the compounds it contains. The ClassyFire webserver is freely accessible at http://classyfire.wishartlab.com/. Moreover, a Ruby API version is available at https://bitbucket.org/wishartlab/classyfire_api, which provides programmatic access to the ClassyFire server and database. ClassyFire has been used to annotate over 77 million compounds and has already been integrated into other software packages to automatically generate textual descriptions for, and/or infer biological properties of over 100,000 compounds. Additional examples and applications are provided in this paper. ClassyFire, in combination with ChemOnt (ClassyFire's comprehensive chemical taxonomy), now allows chemists and cheminformaticians to perform large-scale, rapid and automated chemical classification. Moreover, a freely accessible API allows easy access to more than 77 million "ClassyFire" classified compounds. The results can be used to help annotate well studied, as well as lesser-known compounds. In addition, these chemical classifications can be used as input for data integration, and many other cheminformatics-related tasks.

X Demographics

X Demographics

The data shown below were collected from the profiles of 23 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 662 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 1 <1%
Unknown 661 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 130 20%
Researcher 93 14%
Student > Master 89 13%
Student > Bachelor 65 10%
Student > Postgraduate 25 4%
Other 75 11%
Unknown 185 28%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 110 17%
Chemistry 96 15%
Agricultural and Biological Sciences 92 14%
Computer Science 29 4%
Pharmacology, Toxicology and Pharmaceutical Science 23 3%
Other 96 15%
Unknown 216 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 30. 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 29 January 2024.
All research outputs
#1,338,679
of 25,837,817 outputs
Outputs from Journal of Cheminformatics
#64
of 984 outputs
Outputs of similar age
#23,982
of 319,860 outputs
Outputs of similar age from Journal of Cheminformatics
#2
of 24 outputs
Altmetric has tracked 25,837,817 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 984 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 particularly well, scoring higher than 93% 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 319,860 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% 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 particularly well, scoring higher than 91% of its contemporaries.