QSAR Modelling to Identify LRRK2 Inhibitors for Parkinson's Disease

Autores
Sebastián Pérez, Víctor; Martínez, María Jimena; Gil, Carmen; Campillo Martín, Nuria Eugenia; Martínez, Ana; Ponzoni, Ignacio
Año de publicación
2019
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Parkinson's disease is one of the most common neurodegenerative illnesses in older persons and the leucine-rich repeat kinase 2 (LRRK2) is an auspicious target for its pharmacological treatment. In this work, quantitative structure-activity relationship (QSAR) models for identification of putative inhibitors of LRRK2 protein are developed by using an in-house chemical library and several machine learning techniques. The methodology applied in this paper has two steps: first, alternative subsets of molecular descriptors useful for characterizing LRRK2 inhibitors are chosen by a multi-objective feature selection method; secondly, QSAR models are learned by using these subsets and three different strategies for supervised learning. The qualities of all these QSAR models are compared by classical metrics and the best models are discussed in statistical and physicochemical terms.
Fil: Sebastián Pérez, Víctor. Consejo Superior de Investigaciones Científicas. Centro de Investigaciones Biológicas; España
Fil: Martínez, María Jimena. 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: Gil, Carmen. Consejo Superior de Investigaciones Científicas. Centro de Investigaciones Biológicas; España
Fil: Campillo Martín, Nuria Eugenia. Consejo Superior de Investigaciones Científicas. Centro de Investigaciones Biológicas; España
Fil: Martínez, Ana. Consejo Superior de Investigaciones Científicas. Centro de Investigaciones Biológicas; España
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
CHEMINFORMATICS
LRRK2
MACHINE LEARNING
PARKINSON’S DISEASE
QSAR
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/117434
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/117434
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network_name_str CONICET Digital (CONICET)
spelling QSAR Modelling to Identify LRRK2 Inhibitors for Parkinson's DiseaseSebastián Pérez, VíctorMartínez, María JimenaGil, CarmenCampillo Martín, Nuria EugeniaMartínez, AnaPonzoni, IgnacioCHEMINFORMATICSLRRK2MACHINE LEARNINGPARKINSON’S DISEASEQSARhttps://purl.org/becyt/ford/1.4https://purl.org/becyt/ford/1Parkinson's disease is one of the most common neurodegenerative illnesses in older persons and the leucine-rich repeat kinase 2 (LRRK2) is an auspicious target for its pharmacological treatment. In this work, quantitative structure-activity relationship (QSAR) models for identification of putative inhibitors of LRRK2 protein are developed by using an in-house chemical library and several machine learning techniques. The methodology applied in this paper has two steps: first, alternative subsets of molecular descriptors useful for characterizing LRRK2 inhibitors are chosen by a multi-objective feature selection method; secondly, QSAR models are learned by using these subsets and three different strategies for supervised learning. The qualities of all these QSAR models are compared by classical metrics and the best models are discussed in statistical and physicochemical terms.Fil: Sebastián Pérez, Víctor. Consejo Superior de Investigaciones Científicas. Centro de Investigaciones Biológicas; EspañaFil: Martínez, María Jimena. 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: Gil, Carmen. Consejo Superior de Investigaciones Científicas. Centro de Investigaciones Biológicas; EspañaFil: Campillo Martín, Nuria Eugenia. Consejo Superior de Investigaciones Científicas. Centro de Investigaciones Biológicas; EspañaFil: Martínez, Ana. Consejo Superior de Investigaciones Científicas. Centro de Investigaciones Biológicas; EspañaFil: 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; ArgentinaDe Gruyter2019-02-14info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/117434Sebastián Pérez, Víctor; Martínez, María Jimena; Gil, Carmen; Campillo Martín, Nuria Eugenia; Martínez, Ana; et al.; QSAR Modelling to Identify LRRK2 Inhibitors for Parkinson's Disease; De Gruyter; Journal of integrative bioinformatics; 16; 1; 14-2-2019; 1-8; 201800631613-4516CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.degruyter.com/view/journals/jib/16/1/article-20180063.xmlinfo:eu-repo/semantics/altIdentifier/doi/10.1515/jib-2018-0063info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T15:17:36Zoai:ri.conicet.gov.ar:11336/117434instacron: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:34982025-10-15 15:17:36.794CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv QSAR Modelling to Identify LRRK2 Inhibitors for Parkinson's Disease
title QSAR Modelling to Identify LRRK2 Inhibitors for Parkinson's Disease
spellingShingle QSAR Modelling to Identify LRRK2 Inhibitors for Parkinson's Disease
Sebastián Pérez, Víctor
CHEMINFORMATICS
LRRK2
MACHINE LEARNING
PARKINSON’S DISEASE
QSAR
title_short QSAR Modelling to Identify LRRK2 Inhibitors for Parkinson's Disease
title_full QSAR Modelling to Identify LRRK2 Inhibitors for Parkinson's Disease
title_fullStr QSAR Modelling to Identify LRRK2 Inhibitors for Parkinson's Disease
title_full_unstemmed QSAR Modelling to Identify LRRK2 Inhibitors for Parkinson's Disease
title_sort QSAR Modelling to Identify LRRK2 Inhibitors for Parkinson's Disease
dc.creator.none.fl_str_mv Sebastián Pérez, Víctor
Martínez, María Jimena
Gil, Carmen
Campillo Martín, Nuria Eugenia
Martínez, Ana
Ponzoni, Ignacio
author Sebastián Pérez, Víctor
author_facet Sebastián Pérez, Víctor
Martínez, María Jimena
Gil, Carmen
Campillo Martín, Nuria Eugenia
Martínez, Ana
Ponzoni, Ignacio
author_role author
author2 Martínez, María Jimena
Gil, Carmen
Campillo Martín, Nuria Eugenia
Martínez, Ana
Ponzoni, Ignacio
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv CHEMINFORMATICS
LRRK2
MACHINE LEARNING
PARKINSON’S DISEASE
QSAR
topic CHEMINFORMATICS
LRRK2
MACHINE LEARNING
PARKINSON’S DISEASE
QSAR
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.4
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Parkinson's disease is one of the most common neurodegenerative illnesses in older persons and the leucine-rich repeat kinase 2 (LRRK2) is an auspicious target for its pharmacological treatment. In this work, quantitative structure-activity relationship (QSAR) models for identification of putative inhibitors of LRRK2 protein are developed by using an in-house chemical library and several machine learning techniques. The methodology applied in this paper has two steps: first, alternative subsets of molecular descriptors useful for characterizing LRRK2 inhibitors are chosen by a multi-objective feature selection method; secondly, QSAR models are learned by using these subsets and three different strategies for supervised learning. The qualities of all these QSAR models are compared by classical metrics and the best models are discussed in statistical and physicochemical terms.
Fil: Sebastián Pérez, Víctor. Consejo Superior de Investigaciones Científicas. Centro de Investigaciones Biológicas; España
Fil: Martínez, María Jimena. 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: Gil, Carmen. Consejo Superior de Investigaciones Científicas. Centro de Investigaciones Biológicas; España
Fil: Campillo Martín, Nuria Eugenia. Consejo Superior de Investigaciones Científicas. Centro de Investigaciones Biológicas; España
Fil: Martínez, Ana. Consejo Superior de Investigaciones Científicas. Centro de Investigaciones Biológicas; España
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 Parkinson's disease is one of the most common neurodegenerative illnesses in older persons and the leucine-rich repeat kinase 2 (LRRK2) is an auspicious target for its pharmacological treatment. In this work, quantitative structure-activity relationship (QSAR) models for identification of putative inhibitors of LRRK2 protein are developed by using an in-house chemical library and several machine learning techniques. The methodology applied in this paper has two steps: first, alternative subsets of molecular descriptors useful for characterizing LRRK2 inhibitors are chosen by a multi-objective feature selection method; secondly, QSAR models are learned by using these subsets and three different strategies for supervised learning. The qualities of all these QSAR models are compared by classical metrics and the best models are discussed in statistical and physicochemical terms.
publishDate 2019
dc.date.none.fl_str_mv 2019-02-14
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/11336/117434
Sebastián Pérez, Víctor; Martínez, María Jimena; Gil, Carmen; Campillo Martín, Nuria Eugenia; Martínez, Ana; et al.; QSAR Modelling to Identify LRRK2 Inhibitors for Parkinson's Disease; De Gruyter; Journal of integrative bioinformatics; 16; 1; 14-2-2019; 1-8; 20180063
1613-4516
CONICET Digital
CONICET
url http://hdl.handle.net/11336/117434
identifier_str_mv Sebastián Pérez, Víctor; Martínez, María Jimena; Gil, Carmen; Campillo Martín, Nuria Eugenia; Martínez, Ana; et al.; QSAR Modelling to Identify LRRK2 Inhibitors for Parkinson's Disease; De Gruyter; Journal of integrative bioinformatics; 16; 1; 14-2-2019; 1-8; 20180063
1613-4516
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.degruyter.com/view/journals/jib/16/1/article-20180063.xml
info:eu-repo/semantics/altIdentifier/doi/10.1515/jib-2018-0063
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv De Gruyter
publisher.none.fl_str_mv De Gruyter
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_ 1846083325240803328
score 13.22299

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