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A possible extension to the RInChI as a means of providing machine readable process data

The algorithmic, large-scale use and analysis of reaction databases such as Reaxys is currently hindered by the absence of widely adopted standards for publishing reaction data in machine readable formats. Crucial data such as yields of all products or stoichiometry are frequently not explicitly sta...

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Autores principales: Jacob, Philipp-Maximilian, Lan, Tian, Goodman, Jonathan M., Lapkin, Alexei A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5388667/
https://www.ncbi.nlm.nih.gov/pubmed/29086180
http://dx.doi.org/10.1186/s13321-017-0210-6
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author Jacob, Philipp-Maximilian
Lan, Tian
Goodman, Jonathan M.
Lapkin, Alexei A.
author_facet Jacob, Philipp-Maximilian
Lan, Tian
Goodman, Jonathan M.
Lapkin, Alexei A.
author_sort Jacob, Philipp-Maximilian
collection PubMed
description The algorithmic, large-scale use and analysis of reaction databases such as Reaxys is currently hindered by the absence of widely adopted standards for publishing reaction data in machine readable formats. Crucial data such as yields of all products or stoichiometry are frequently not explicitly stated in the published papers and, hence, not reported in the database entry for those reactions, limiting their usefulness for algorithmic analysis. This paper presents a possible extension to the IUPAC RInChI standard via an auxiliary layer, termed ProcAuxInfo, which is a standardised, extensible form in which to report certain key reaction parameters such as declaration of all products and reactants as well as auxiliaries known in the reaction, reaction stoichiometry, amounts of substances used, conversion, yield and operating conditions. The standard is demonstrated via creation of the RInChI including the ProcAuxInfo layer based on three published reactions and demonstrates accurate data recoverability via reverse translation of the created strings. Implementation of this or another method of reporting process data by the publishing community would ensure that databases, such as Reaxys, would be able to abstract crucial data for big data analysis of their contents.
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spelling pubmed-53886672017-04-27 A possible extension to the RInChI as a means of providing machine readable process data Jacob, Philipp-Maximilian Lan, Tian Goodman, Jonathan M. Lapkin, Alexei A. J Cheminform Research Article The algorithmic, large-scale use and analysis of reaction databases such as Reaxys is currently hindered by the absence of widely adopted standards for publishing reaction data in machine readable formats. Crucial data such as yields of all products or stoichiometry are frequently not explicitly stated in the published papers and, hence, not reported in the database entry for those reactions, limiting their usefulness for algorithmic analysis. This paper presents a possible extension to the IUPAC RInChI standard via an auxiliary layer, termed ProcAuxInfo, which is a standardised, extensible form in which to report certain key reaction parameters such as declaration of all products and reactants as well as auxiliaries known in the reaction, reaction stoichiometry, amounts of substances used, conversion, yield and operating conditions. The standard is demonstrated via creation of the RInChI including the ProcAuxInfo layer based on three published reactions and demonstrates accurate data recoverability via reverse translation of the created strings. Implementation of this or another method of reporting process data by the publishing community would ensure that databases, such as Reaxys, would be able to abstract crucial data for big data analysis of their contents. Springer International Publishing 2017-04-11 /pmc/articles/PMC5388667/ /pubmed/29086180 http://dx.doi.org/10.1186/s13321-017-0210-6 Text en © The Author(s) 2017 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.
spellingShingle Research Article
Jacob, Philipp-Maximilian
Lan, Tian
Goodman, Jonathan M.
Lapkin, Alexei A.
A possible extension to the RInChI as a means of providing machine readable process data
title A possible extension to the RInChI as a means of providing machine readable process data
title_full A possible extension to the RInChI as a means of providing machine readable process data
title_fullStr A possible extension to the RInChI as a means of providing machine readable process data
title_full_unstemmed A possible extension to the RInChI as a means of providing machine readable process data
title_short A possible extension to the RInChI as a means of providing machine readable process data
title_sort possible extension to the rinchi as a means of providing machine readable process data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5388667/
https://www.ncbi.nlm.nih.gov/pubmed/29086180
http://dx.doi.org/10.1186/s13321-017-0210-6
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