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Principles and requirements for nanomaterial representations to facilitate machine processing and cooperation with nanoinformatics tools

Efficient and machine-readable representations are needed to accurately identify, validate and communicate information of chemical structures. Many such representations have been developed (as, for example, the Simplified Molecular-Input Line-Entry System and the IUPAC International Chemical Identif...

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Autores principales: Blekos, Kostas, Chairetakis, Kostas, Lynch, Iseult, Marcoulaki, Effie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10099932/
https://www.ncbi.nlm.nih.gov/pubmed/37046286
http://dx.doi.org/10.1186/s13321-022-00669-6
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author Blekos, Kostas
Chairetakis, Kostas
Lynch, Iseult
Marcoulaki, Effie
author_facet Blekos, Kostas
Chairetakis, Kostas
Lynch, Iseult
Marcoulaki, Effie
author_sort Blekos, Kostas
collection PubMed
description Efficient and machine-readable representations are needed to accurately identify, validate and communicate information of chemical structures. Many such representations have been developed (as, for example, the Simplified Molecular-Input Line-Entry System and the IUPAC International Chemical Identifier), each offering advantages specific to various use-cases. Representation of the multi-component structures of nanomaterials (NMs), though, remains out of scope for all the currently available standards, as the nature of NMs sets new challenges on formalizing the encoding of their structure, interactions and environmental parameters. In this work we identify a set of principles that a NM representation should adhere to in order to provide “machine-friendly” encodings of NMs, i.e. encodings that facilitate machine processing and cooperation with nanoinformatics tools. We illustrate our principles by showing how the recently introduced InChI-based NM representation, might be augmented, in principle, to also encode morphology and mixture properties, distributions of properties, and also to capture auxiliary information and allow data reuse.
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spelling pubmed-100999322023-04-14 Principles and requirements for nanomaterial representations to facilitate machine processing and cooperation with nanoinformatics tools Blekos, Kostas Chairetakis, Kostas Lynch, Iseult Marcoulaki, Effie J Cheminform Research Efficient and machine-readable representations are needed to accurately identify, validate and communicate information of chemical structures. Many such representations have been developed (as, for example, the Simplified Molecular-Input Line-Entry System and the IUPAC International Chemical Identifier), each offering advantages specific to various use-cases. Representation of the multi-component structures of nanomaterials (NMs), though, remains out of scope for all the currently available standards, as the nature of NMs sets new challenges on formalizing the encoding of their structure, interactions and environmental parameters. In this work we identify a set of principles that a NM representation should adhere to in order to provide “machine-friendly” encodings of NMs, i.e. encodings that facilitate machine processing and cooperation with nanoinformatics tools. We illustrate our principles by showing how the recently introduced InChI-based NM representation, might be augmented, in principle, to also encode morphology and mixture properties, distributions of properties, and also to capture auxiliary information and allow data reuse. Springer International Publishing 2023-04-12 /pmc/articles/PMC10099932/ /pubmed/37046286 http://dx.doi.org/10.1186/s13321-022-00669-6 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Blekos, Kostas
Chairetakis, Kostas
Lynch, Iseult
Marcoulaki, Effie
Principles and requirements for nanomaterial representations to facilitate machine processing and cooperation with nanoinformatics tools
title Principles and requirements for nanomaterial representations to facilitate machine processing and cooperation with nanoinformatics tools
title_full Principles and requirements for nanomaterial representations to facilitate machine processing and cooperation with nanoinformatics tools
title_fullStr Principles and requirements for nanomaterial representations to facilitate machine processing and cooperation with nanoinformatics tools
title_full_unstemmed Principles and requirements for nanomaterial representations to facilitate machine processing and cooperation with nanoinformatics tools
title_short Principles and requirements for nanomaterial representations to facilitate machine processing and cooperation with nanoinformatics tools
title_sort principles and requirements for nanomaterial representations to facilitate machine processing and cooperation with nanoinformatics tools
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10099932/
https://www.ncbi.nlm.nih.gov/pubmed/37046286
http://dx.doi.org/10.1186/s13321-022-00669-6
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