Transformer-based artificial neural networks for the conversion between chemical notations

We developed a Transformer-based artificial neural approach to translate between SMILES and IUPAC chemical notations: Struct2IUPAC and IUPAC2Struct. The overall performance level of our model is comparable to the rule-based solutions. We proved that the accuracy and speed of computations as well as...

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Autores principales: Krasnov, Lev, Khokhlov, Ivan, Fedorov, Maxim V., Sosnin, Sergey
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8292511/
https://www.ncbi.nlm.nih.gov/pubmed/34285269
http://dx.doi.org/10.1038/s41598-021-94082-y
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author Krasnov, Lev
Khokhlov, Ivan
Fedorov, Maxim V.
Sosnin, Sergey
author_facet Krasnov, Lev
Khokhlov, Ivan
Fedorov, Maxim V.
Sosnin, Sergey
author_sort Krasnov, Lev
collection PubMed
description We developed a Transformer-based artificial neural approach to translate between SMILES and IUPAC chemical notations: Struct2IUPAC and IUPAC2Struct. The overall performance level of our model is comparable to the rule-based solutions. We proved that the accuracy and speed of computations as well as the robustness of the model allow to use it in production. Our showcase demonstrates that a neural-based solution can facilitate rapid development keeping the required level of accuracy. We believe that our findings will inspire other developers to reduce development costs by replacing complex rule-based solutions with neural-based ones.
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spelling pubmed-82925112021-07-22 Transformer-based artificial neural networks for the conversion between chemical notations Krasnov, Lev Khokhlov, Ivan Fedorov, Maxim V. Sosnin, Sergey Sci Rep Article We developed a Transformer-based artificial neural approach to translate between SMILES and IUPAC chemical notations: Struct2IUPAC and IUPAC2Struct. The overall performance level of our model is comparable to the rule-based solutions. We proved that the accuracy and speed of computations as well as the robustness of the model allow to use it in production. Our showcase demonstrates that a neural-based solution can facilitate rapid development keeping the required level of accuracy. We believe that our findings will inspire other developers to reduce development costs by replacing complex rule-based solutions with neural-based ones. Nature Publishing Group UK 2021-07-20 /pmc/articles/PMC8292511/ /pubmed/34285269 http://dx.doi.org/10.1038/s41598-021-94082-y Text en © The Author(s) 2021 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/) .
spellingShingle Article
Krasnov, Lev
Khokhlov, Ivan
Fedorov, Maxim V.
Sosnin, Sergey
Transformer-based artificial neural networks for the conversion between chemical notations
title Transformer-based artificial neural networks for the conversion between chemical notations
title_full Transformer-based artificial neural networks for the conversion between chemical notations
title_fullStr Transformer-based artificial neural networks for the conversion between chemical notations
title_full_unstemmed Transformer-based artificial neural networks for the conversion between chemical notations
title_short Transformer-based artificial neural networks for the conversion between chemical notations
title_sort transformer-based artificial neural networks for the conversion between chemical notations
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8292511/
https://www.ncbi.nlm.nih.gov/pubmed/34285269
http://dx.doi.org/10.1038/s41598-021-94082-y
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