Procedures for Converting Systematic Names of Organic Compounds into Atom-Bond Connection Tables

Stouw, Gerald G. Vander; Naznitsky, I.; Rush, James E.

doi:10.1021/c160026a009

Cited by 21 publications

(15 citation statements)

References 2 publications

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“…Earlier works on the direct conversion of names to formulas were developed by Garfield in the 1960s, using a dictionary of name parts or morphemes . CAS then reported internal procedures based on nomenclature rules for the automatic conversion of CAS names and other systematic nomenclatures into chemical structures. , Later, in 1989, Cooke and colleagues applied grammar-based techniques to the recognition of IUPAC systematic chemical nomenclature and its translation to structure diagrams. − Soon after, rule-based approaches prompted as an alternative to grammar-based approaches for systematic nomenclature, with the first commercial program, named Name = Struct, being launched in 1999 …”

Section: Linking Documents To Structuresmentioning

confidence: 99%

Information Retrieval and Text Mining Technologies for Chemistry

et al. 2017

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Efficient access to chemical information contained in scientific literature, patents, technical reports, or the web is a pressing need shared by researchers and patent attorneys from different chemical disciplines. Retrieval of important chemical information in most cases starts with finding relevant documents for a particular chemical compound or family. Targeted retrieval of chemical documents is closely connected to the automatic recognition of chemical entities in the text, which commonly involves the extraction of the entire list of chemicals mentioned in a document, including any associated information. In this Review, we provide a comprehensive and in-depth description of fundamental concepts, technical implementations, and current technologies for meeting these information demands. A strong focus is placed on community challenges addressing systems performance, more particularly CHEMDNER and CHEMDNER patents tasks of BioCreative IV and V, respectively. Considering the growing interest in the construction of automatically annotated chemical knowledge bases that integrate chemical information and biological data, cheminformatics approaches for mapping the extracted chemical names into chemical structures and their subsequent annotation together with text mining applications for linking chemistry with biological information are also presented. Finally, future trends and current challenges are highlighted as a roadmap proposal for research in this emerging field.

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Section: Linking Documents To Structuresmentioning

confidence: 99%

Information Retrieval and Text Mining Technologies for Chemistry

et al. 2017

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“…The main concepts outlined in this work were the linguistic analysis of chemical nomenclature and its interpretation and conversion to chemical structure representations. Within just a few years, significant progress in the development of procedures to convert chemical names into structures has been achieved,13,14 and a comprehensive analysis of name–to–structure conversion software tools has been performed 15,16. In contrast to this situation, the challenge of chemical name generation from chemical structures was addressed only for von Baeyer polycyclic systems17 for which nomenclature principles had been developed in an algorithmic manner suitable for computer‐based implementation.…”

Section: The History and Principles Of Automated Chemical Nomenclaturmentioning

confidence: 99%

Automated systematic nomenclature generation for organic compounds

Williams¹,

Yerin²

2012

WIREs Comput Mol Sci

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The availability of systematic chemical names allows for the simple textual exchange of chemical structure information encoded into a name. The exchange of molecular structures in graphical format, molecular connection tables, or text strings encoding the molecule (e.g., SMILES and InChIs) is well established in cheminformatics and a plethora of chemical structure drawing tools exist to facilitate this exchange. However, even with well‐established systematic naming rules, software tools to allow for the generation of chemical names from structural inputs, and the reversal of these systematic names back to the original chemical structure, have only been available for a short time relative to the rules themselves. Although early tools were lacking in both capability and quality, they have matured over the years to provide excellent tools for chemists. Here, we review the capabilities of existing systematic naming software algorithms and tools and review some of the challenges, limitations, and future challenges for development. © 2012 John Wiley & Sons, Ltd. This article is categorized under: Computer and Information Science > Chemoinformatics

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“…Van der Stouw, et al, proposed a method to convert chemical names to chemical formula [Vander Stouw et al 1967] used by the Chemical Abstract Service in 1967 that processes chemical names from left to right. However, their description of the algorithm remains anecdotal.…”

Section: Chemical Name Segmentationmentioning

confidence: 99%

“…They do not provide enough details for us to re-implement their algorithm. They claim that their algorithm works in about 60% of the cases and with additional studies can be enhanced to cover "80 to 85% of names of carbon-containing compounds" [Vander Stouw et al 1967]. The Chemical Abstract Service has also published the Registry File Basic Name Segment Dictionary in 1993 [ACS 1993].…”

Section: Chemical Name Segmentationmentioning

confidence: 99%

Identifying, Indexing, and Ranking Chemical Formulae and Chemical Names in Digital Documents

Sun

Mitra

Giles

et al. 2011

ACM Trans. Inf. Syst.

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End users use chemical search engines to search for chemical formulae and chemical names. Chemical search engines identify and index chemical formulae and chemical names appearing in text documents to support efficient search and retrieval in the future. Identifying chemical formulae and chemical names in text automatically has been a hard problem that has met with varying degrees of success in the past. We propose algorithms for chemical formula and chemical name tagging using Conditional Random Fields (CRFs) and Support Vector Machines (SVMs) that achieve higher accuracy than existing (published) methods. After chemical entities have been identified in text documents, they must be indexed. In order to support user-provided search queries that require a partial match between the chemical name segment used as a keyword or a partial chemical formula, all possible (or a significant number of) sub-formulae of formulae that appear in any document and all possible sub-terms (e.g., 'methyl') of chemical names (e.g., 'methylethyl ketone') must be indexed. Indexing all possible sub-formulae and sub-terms results in an exponential increase in the storage and memory requirements, and the time taken to process the indices. We propose techniques to prune the indices significantly without reducing the quality of the returned results significantly. Finally, we propose multiple query semantics to allow users to pose different types of partial search queries for chemical entities. We demonstrate empirically that our search engines improve the relevance of the returned results for search queries involving chemical entities.

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Procedures for Converting Systematic Names of Organic Compounds into Atom-Bond Connection Tables

Cited by 21 publications

References 2 publications

Information Retrieval and Text Mining Technologies for Chemistry

Information Retrieval and Text Mining Technologies for Chemistry

Automated systematic nomenclature generation for organic compounds

Identifying, Indexing, and Ranking Chemical Formulae and Chemical Names in Digital Documents

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