How can polydispersity information be integrated in the QSPR modeling of mechanical properties?

Autores
Cravero, Fiorella; Schustik, Santiago; Martinez Amezaga, Nancy María Jimena; Diaz, Monica Fatima; Ponzoni, Ignacio
Año de publicación
2022
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Polymer informatics is an emerging discipline that has benefited from the strong development that data science has experienced over the last decade. In particular, machine learning methods are useful to infer QSPR (Quantitative Structure Property Relationships) models that allow predicting mechanical properties related to the industrial profile of polymeric materials based on their structural repeating units (SRUs). Nonetheless, the chemical structure of the SRU is only one of the many factors that affect the industrial usefulness of a polymer. Other equally relevant factors are polymer molecular weight, molecular weight distribution, and production method, which are related to the inherent polydispersity of this kind of material. For this reason, the computational characterization used for the building of QSPR models for predicting mechanical properties should consider these main factors. The aim of this paper is to highlight recent advances in data science to address the inclusion of polydispersity information of polymeric materials in QSPR modeling. We present two dimensions of discussion: data representation and algorithmic issues. In the first one, we examine how different strategies can be applied to include polydispersity data in the molecular descriptors that characterize the polymers. We explain two data representation approaches designed by our group, named as trivalued and multivalued molecular descriptors. In the second dimension, we discuss algorithms proposed to deal with these new molecular descriptor representations during the construction of the QSPR models. Thus, we present here a comprehensible and integral methodology to address the challenges that polydispersity generates in the QSPR modeling of mechanical properties of polymers.
Fil: Cravero, Fiorella. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina
Fil: Schustik, Santiago. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina
Fil: Martinez Amezaga, Nancy María Jimena. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
Fil: Diaz, Monica Fatima. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina
Fil: Ponzoni, Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina
Materia
POLYMER INFORMATICS
MACHINE LEARNING
QSAR
POLYDISPERSITY
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/162113
Acceder
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network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling How can polydispersity information be integrated in the QSPR modeling of mechanical properties?Cravero, FiorellaSchustik, SantiagoMartinez Amezaga, Nancy María JimenaDiaz, Monica FatimaPonzoni, IgnacioPOLYMER INFORMATICSMACHINE LEARNINGQSARPOLYDISPERSITYhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Polymer informatics is an emerging discipline that has benefited from the strong development that data science has experienced over the last decade. In particular, machine learning methods are useful to infer QSPR (Quantitative Structure Property Relationships) models that allow predicting mechanical properties related to the industrial profile of polymeric materials based on their structural repeating units (SRUs). Nonetheless, the chemical structure of the SRU is only one of the many factors that affect the industrial usefulness of a polymer. Other equally relevant factors are polymer molecular weight, molecular weight distribution, and production method, which are related to the inherent polydispersity of this kind of material. For this reason, the computational characterization used for the building of QSPR models for predicting mechanical properties should consider these main factors. The aim of this paper is to highlight recent advances in data science to address the inclusion of polydispersity information of polymeric materials in QSPR modeling. We present two dimensions of discussion: data representation and algorithmic issues. In the first one, we examine how different strategies can be applied to include polydispersity data in the molecular descriptors that characterize the polymers. We explain two data representation approaches designed by our group, named as trivalued and multivalued molecular descriptors. In the second dimension, we discuss algorithms proposed to deal with these new molecular descriptor representations during the construction of the QSPR models. Thus, we present here a comprehensible and integral methodology to address the challenges that polydispersity generates in the QSPR modeling of mechanical properties of polymers.Fil: Cravero, Fiorella. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Schustik, Santiago. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; ArgentinaFil: Martinez Amezaga, Nancy María Jimena. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Diaz, Monica Fatima. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; ArgentinaFil: Ponzoni, Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaTaylor & Francis2022-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/162113Cravero, Fiorella; Schustik, Santiago; Martinez Amezaga, Nancy María Jimena; Diaz, Monica Fatima; Ponzoni, Ignacio; How can polydispersity information be integrated in the QSPR modeling of mechanical properties?; Taylor & Francis; Science and Technology of Advanced Materials: Methods; 2; 1; 1-2022; 1-142766-0400CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.tandfonline.com/doi/full/10.1080/27660400.2021.2012540info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T12:08:58Zoai:ri.conicet.gov.ar:11336/162113instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-22 12:08:59.216CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv How can polydispersity information be integrated in the QSPR modeling of mechanical properties?
title How can polydispersity information be integrated in the QSPR modeling of mechanical properties?
spellingShingle How can polydispersity information be integrated in the QSPR modeling of mechanical properties?
Cravero, Fiorella
POLYMER INFORMATICS
MACHINE LEARNING
QSAR
POLYDISPERSITY
title_short How can polydispersity information be integrated in the QSPR modeling of mechanical properties?
title_full How can polydispersity information be integrated in the QSPR modeling of mechanical properties?
title_fullStr How can polydispersity information be integrated in the QSPR modeling of mechanical properties?
title_full_unstemmed How can polydispersity information be integrated in the QSPR modeling of mechanical properties?
title_sort How can polydispersity information be integrated in the QSPR modeling of mechanical properties?
dc.creator.none.fl_str_mv Cravero, Fiorella
Schustik, Santiago
Martinez Amezaga, Nancy María Jimena
Diaz, Monica Fatima
Ponzoni, Ignacio
author Cravero, Fiorella
author_facet Cravero, Fiorella
Schustik, Santiago
Martinez Amezaga, Nancy María Jimena
Diaz, Monica Fatima
Ponzoni, Ignacio
author_role author
author2 Schustik, Santiago
Martinez Amezaga, Nancy María Jimena
Diaz, Monica Fatima
Ponzoni, Ignacio
author2_role author
author
author
author
dc.subject.none.fl_str_mv POLYMER INFORMATICS
MACHINE LEARNING
QSAR
POLYDISPERSITY
topic POLYMER INFORMATICS
MACHINE LEARNING
QSAR
POLYDISPERSITY
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Polymer informatics is an emerging discipline that has benefited from the strong development that data science has experienced over the last decade. In particular, machine learning methods are useful to infer QSPR (Quantitative Structure Property Relationships) models that allow predicting mechanical properties related to the industrial profile of polymeric materials based on their structural repeating units (SRUs). Nonetheless, the chemical structure of the SRU is only one of the many factors that affect the industrial usefulness of a polymer. Other equally relevant factors are polymer molecular weight, molecular weight distribution, and production method, which are related to the inherent polydispersity of this kind of material. For this reason, the computational characterization used for the building of QSPR models for predicting mechanical properties should consider these main factors. The aim of this paper is to highlight recent advances in data science to address the inclusion of polydispersity information of polymeric materials in QSPR modeling. We present two dimensions of discussion: data representation and algorithmic issues. In the first one, we examine how different strategies can be applied to include polydispersity data in the molecular descriptors that characterize the polymers. We explain two data representation approaches designed by our group, named as trivalued and multivalued molecular descriptors. In the second dimension, we discuss algorithms proposed to deal with these new molecular descriptor representations during the construction of the QSPR models. Thus, we present here a comprehensible and integral methodology to address the challenges that polydispersity generates in the QSPR modeling of mechanical properties of polymers.
Fil: Cravero, Fiorella. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina
Fil: Schustik, Santiago. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina
Fil: Martinez Amezaga, Nancy María Jimena. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
Fil: Diaz, Monica Fatima. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina
Fil: Ponzoni, Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina
description Polymer informatics is an emerging discipline that has benefited from the strong development that data science has experienced over the last decade. In particular, machine learning methods are useful to infer QSPR (Quantitative Structure Property Relationships) models that allow predicting mechanical properties related to the industrial profile of polymeric materials based on their structural repeating units (SRUs). Nonetheless, the chemical structure of the SRU is only one of the many factors that affect the industrial usefulness of a polymer. Other equally relevant factors are polymer molecular weight, molecular weight distribution, and production method, which are related to the inherent polydispersity of this kind of material. For this reason, the computational characterization used for the building of QSPR models for predicting mechanical properties should consider these main factors. The aim of this paper is to highlight recent advances in data science to address the inclusion of polydispersity information of polymeric materials in QSPR modeling. We present two dimensions of discussion: data representation and algorithmic issues. In the first one, we examine how different strategies can be applied to include polydispersity data in the molecular descriptors that characterize the polymers. We explain two data representation approaches designed by our group, named as trivalued and multivalued molecular descriptors. In the second dimension, we discuss algorithms proposed to deal with these new molecular descriptor representations during the construction of the QSPR models. Thus, we present here a comprehensible and integral methodology to address the challenges that polydispersity generates in the QSPR modeling of mechanical properties of polymers.
publishDate 2022
dc.date.none.fl_str_mv 2022-01
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/162113
Cravero, Fiorella; Schustik, Santiago; Martinez Amezaga, Nancy María Jimena; Diaz, Monica Fatima; Ponzoni, Ignacio; How can polydispersity information be integrated in the QSPR modeling of mechanical properties?; Taylor & Francis; Science and Technology of Advanced Materials: Methods; 2; 1; 1-2022; 1-14
2766-0400
CONICET Digital
CONICET
url http://hdl.handle.net/11336/162113
identifier_str_mv Cravero, Fiorella; Schustik, Santiago; Martinez Amezaga, Nancy María Jimena; Diaz, Monica Fatima; Ponzoni, Ignacio; How can polydispersity information be integrated in the QSPR modeling of mechanical properties?; Taylor & Francis; Science and Technology of Advanced Materials: Methods; 2; 1; 1-2022; 1-14
2766-0400
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.tandfonline.com/doi/full/10.1080/27660400.2021.2012540
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Taylor & Francis
publisher.none.fl_str_mv Taylor & Francis
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 12.928904

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