Characterising the glass transition temperature-structure relationship through a recurrent neural network

Quantitative structure-property relationship (QSPR) is a powerful analytical method to find correlations between the structure of a molecule and its physicochemical properties. The glass transition temperature (Tg) is one of the most reported properties, and its characterisation is critical for tuni...

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Detalles Bibliográficos
Autores: Borredon, Claudia, Miccio, Luis A., Cerveny, Silvina, Schwartz, Gustavo A.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2023
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/342175
Acceso en línea:http://hdl.handle.net/10261/342175
Access Level:acceso abierto
Palabra clave:QSPR
Machine learning
Molecular glass former
Amino acid
RNN
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spelling Characterising the glass transition temperature-structure relationship through a recurrent neural networkBorredon, ClaudiaMiccio, Luis A.Cerveny, SilvinaSchwartz, Gustavo A.QSPRMachine learningMolecular glass formerAmino acidRNNQuantitative structure-property relationship (QSPR) is a powerful analytical method to find correlations between the structure of a molecule and its physicochemical properties. The glass transition temperature (Tg) is one of the most reported properties, and its characterisation is critical for tuning the physical properties of materials. In this work, we explore the use of machine learning in the field of QSPR by developing a recurrent neural network (RNN) that relates the chemical structure and the glass transition temperature of molecular glass formers. In addition, we performed a chemical embedding from the last hidden layer of the RNN architecture into an m-dimensional Tg-oriented space. Then, we test the model to predict the glass transition temperature of essential amino acids and peptides. The results are very promising and they can open the door for exploring and designing new materials.We gratefully acknowledge the financial support from the Spanish Government Ministerio de Ciencia e Innovación Research Project PID2019-104650GB-C21 / PID2022-138070NB-C22 MCIN/ AEI /10.13039/501100011033 and the Basque Government (IT-1566-22). We also acknowledge the support of NVIDIA Corporation with the donation of two GPUs used for this research.We also acknowledge the support of the publication fee by the CSIC Open Access Publication Support Initiative through its Unit of Information Resources for Research (URICI).Peer reviewedElsevierMinisterio de Ciencia, Innovación y Universidades (España)Agencia Estatal de Investigación (España)Eusko JaurlaritzaCSIC - Unidad de Recursos de Información Científica para la Investigación (URICI)Consejo Superior de Investigaciones Científicas (España)NVIDIA CorporationConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202420242023info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10261/342175reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-104650GB-C21info:eu-repo/grantAgreement/AEI//PID2022-138070NB-C22Borredon, Claudia; Miccio, Luis A.; Cerveny, Silvina; Schwartz, Gustavo A.; 2023; Appendix A. Supplementary data. Supplementary information of Characterising the glass transition temperature-structure relationship through a recurrent neural network [Dataset]; Elsevier; https://doi.org/10.1016/j.nocx.2023.100185https://doi.org/10.1016/j.nocx.2023.100185Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3421752026-05-22T06:33:51Z
dc.title.none.fl_str_mv Characterising the glass transition temperature-structure relationship through a recurrent neural network
title Characterising the glass transition temperature-structure relationship through a recurrent neural network
spellingShingle Characterising the glass transition temperature-structure relationship through a recurrent neural network
Borredon, Claudia
QSPR
Machine learning
Molecular glass former
Amino acid
RNN
title_short Characterising the glass transition temperature-structure relationship through a recurrent neural network
title_full Characterising the glass transition temperature-structure relationship through a recurrent neural network
title_fullStr Characterising the glass transition temperature-structure relationship through a recurrent neural network
title_full_unstemmed Characterising the glass transition temperature-structure relationship through a recurrent neural network
title_sort Characterising the glass transition temperature-structure relationship through a recurrent neural network
dc.creator.none.fl_str_mv Borredon, Claudia
Miccio, Luis A.
Cerveny, Silvina
Schwartz, Gustavo A.
author Borredon, Claudia
author_facet Borredon, Claudia
Miccio, Luis A.
Cerveny, Silvina
Schwartz, Gustavo A.
author_role author
author2 Miccio, Luis A.
Cerveny, Silvina
Schwartz, Gustavo A.
author2_role author
author
author
dc.contributor.none.fl_str_mv Ministerio de Ciencia, Innovación y Universidades (España)
Agencia Estatal de Investigación (España)
Eusko Jaurlaritza
CSIC - Unidad de Recursos de Información Científica para la Investigación (URICI)
Consejo Superior de Investigaciones Científicas (España)
NVIDIA Corporation
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv QSPR
Machine learning
Molecular glass former
Amino acid
RNN
topic QSPR
Machine learning
Molecular glass former
Amino acid
RNN
description Quantitative structure-property relationship (QSPR) is a powerful analytical method to find correlations between the structure of a molecule and its physicochemical properties. The glass transition temperature (Tg) is one of the most reported properties, and its characterisation is critical for tuning the physical properties of materials. In this work, we explore the use of machine learning in the field of QSPR by developing a recurrent neural network (RNN) that relates the chemical structure and the glass transition temperature of molecular glass formers. In addition, we performed a chemical embedding from the last hidden layer of the RNN architecture into an m-dimensional Tg-oriented space. Then, we test the model to predict the glass transition temperature of essential amino acids and peptides. The results are very promising and they can open the door for exploring and designing new materials.
publishDate 2023
dc.date.none.fl_str_mv 2023
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/342175
url http://hdl.handle.net/10261/342175
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-104650GB-C21
info:eu-repo/grantAgreement/AEI//PID2022-138070NB-C22
Borredon, Claudia; Miccio, Luis A.; Cerveny, Silvina; Schwartz, Gustavo A.; 2023; Appendix A. Supplementary data. Supplementary information of Characterising the glass transition temperature-structure relationship through a recurrent neural network [Dataset]; Elsevier; https://doi.org/10.1016/j.nocx.2023.100185
https://doi.org/10.1016/j.nocx.2023.100185
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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