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      ClassyFire: automated chemical classification with a comprehensive, computable taxonomy

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          Abstract

          Background

          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.

          Results

          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.

          Conclusion

          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.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s13321-016-0174-y) contains supplementary material, which is available to authorized users.

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          Most cited references22

          • Record: found
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          Gene Ontology: tool for the unification of biology

          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Description of several chemical structure file formats used by computer programs developed at Molecular Design Limited

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              T3DB: the toxic exposome database

              The exposome is defined as the totality of all human environmental exposures from conception to death. It is often regarded as the complement to the genome, with the interaction between the exposome and the genome ultimately determining one's phenotype. The ‘toxic exposome’ is the complete collection of chronically or acutely toxic compounds to which humans can be exposed. Considerable interest in defining the toxic exposome has been spurred on by the realization that most human injuries, deaths and diseases are directly or indirectly caused by toxic substances found in the air, water, food, home or workplace. The Toxin-Toxin-Target Database (T3DB - www.t3db.ca) is a resource that was specifically designed to capture information about the toxic exposome. Originally released in 2010, the first version of T3DB contained data on nearly 2900 common toxic substances along with detailed information on their chemical properties, descriptions, targets, toxic effects, toxicity thresholds, sequences (for both targets and toxins), mechanisms and references. To more closely align itself with the needs of epidemiologists, toxicologists and exposome scientists, the latest release of T3DB has been substantially upgraded to include many more compounds (>3600), targets (>2000) and gene expression datasets (>15 000 genes). It now includes extensive data on ‘normal’ toxic compound concentrations in human biofluids as well as detailed chemical taxonomies, informative chemical ontologies and a large number of referential NMR, MS/MS and GC-MS spectra. This manuscript describes the most recent update to the T3DB, which was previously featured in the 2010 NAR Database Issue.
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                Author and article information

                Contributors
                djoumbou@ualberta.ca
                reisner@ualberta.ca
                cknox@ualberta.ca
                leonid.chepelev@gmail.com
                hastings@ebi.ac.uk
                gowen@ebi.ac.uk
                efahy@ucsd.edu
                steinbeck@ebi.ac.uk
                shankar@ucsd.edu
                bolton@ncbi.nlm.nih.gov
                rgreiner@ualberta.ca
                david.wishart@ualberta.ca
                Journal
                J Cheminform
                J Cheminform
                Journal of Cheminformatics
                Springer International Publishing (Cham )
                1758-2946
                4 November 2016
                4 November 2016
                2016
                : 8
                : 61
                Affiliations
                [1 ]Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E8 Canada
                [2 ]Jobber – Field Service Software, 10520 Jasper Ave, Edmonton, AB T5J 1Z7 Canada
                [3 ]Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8 Canada
                [4 ]National Research Council, National Institute for Nanotechnology (NINT), Edmonton, AB T6G 2M9 Canada
                [5 ]Department of Medical Imaging, The Ottawa Hospital, University of Ottawa, Civic Campus, 1053 Carling Ave, Ottawa, ON K1Y 4E9 Canada
                [6 ]European Molecular Biology Laboratory - European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge UK
                [7 ]Department of Bioengineering, University of California, La Jolla, San Diego, CA 92093 USA
                [8 ]Department of Health and Human Services, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD 20894 USA
                [9 ]Department of Computing Science 2-21 Athabasca Hall, Alberta Innovates Centre for Machine Learning (AICML), University of Alberta, Edmonton, AB T6G 2E8 Canada
                [10 ]The Metabolomics Innovation Center, University of Alberta, Edmonton, AB T6G 2E9 Canada
                Article
                174
                10.1186/s13321-016-0174-y
                5096306
                27867422
                6c95432b-4640-4d0b-be01-dfbe90896bb5
                © The Author(s) 2016

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 30 June 2016
                : 18 October 2016
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100008762, Genome Canada;
                Funded by: Genome Alberta
                Funded by: The Canadian Institutes of Health Research
                Funded by: Alberta Innovates
                Funded by: The National Research Council
                Funded by: The National Institute of Nanotechnology
                Categories
                Software
                Custom metadata
                © The Author(s) 2016

                Chemoinformatics
                structure-based classification,ontology,taxonomy,text-based search,inference,annotation,database,data integration

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