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

Abstract 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...

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Autores principales:	Lev Krasnov, Ivan Khokhlov, Maxim V. Fedorov, Sergey Sosnin
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/1ab182b42dd9410f8a7644dd9fb5bc4f
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spelling	oai:doaj.org-article:1ab182b42dd9410f8a7644dd9fb5bc4f2021-12-02T17:56:56ZTransformer-based artificial neural networks for the conversion between chemical notations10.1038/s41598-021-94082-y2045-2322https://doaj.org/article/1ab182b42dd9410f8a7644dd9fb5bc4f2021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-94082-yhttps://doaj.org/toc/2045-2322Abstract 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.Lev KrasnovIvan KhokhlovMaxim V. FedorovSergey SosninNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Lev Krasnov Ivan Khokhlov Maxim V. Fedorov Sergey Sosnin Transformer-based artificial neural networks for the conversion between chemical notations
description	Abstract 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.
format	article
author	Lev Krasnov Ivan Khokhlov Maxim V. Fedorov Sergey Sosnin
author_facet	Lev Krasnov Ivan Khokhlov Maxim V. Fedorov Sergey Sosnin
author_sort	Lev Krasnov
title	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_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_sort	transformer-based artificial neural networks for the conversion between chemical notations
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/1ab182b42dd9410f8a7644dd9fb5bc4f
work_keys_str_mv	AT levkrasnov transformerbasedartificialneuralnetworksfortheconversionbetweenchemicalnotations AT ivankhokhlov transformerbasedartificialneuralnetworksfortheconversionbetweenchemicalnotations AT maximvfedorov transformerbasedartificialneuralnetworksfortheconversionbetweenchemicalnotations AT sergeysosnin transformerbasedartificialneuralnetworksfortheconversionbetweenchemicalnotations
_version_	1718379031919853568

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

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