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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/162113
Ver los metadatos del registro completo
id |
CONICETDig_6d0cf8e5afbafa29e5e3ace54b5eb057 |
---|---|
oai_identifier_str |
oai:ri.conicet.gov.ar:11336/162113 |
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 Técnicas2025-09-29T10:36:41Zoai: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:34982025-09-29 10:36:41.555CONICET 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 |
_version_ |
1844614387540688896 |
score |
13.070432 |