SAnDReS 2.0: Development of machine-learning models to explore the scoring function space

Autores
de Azevedo, Walter Filgueira Jr.; Rodrigo, Quiroga; Villarreal, Marcos Ariel; Freitas da Silveira, Nelson José; Bitencourt-Ferreira, Gabriela; Duarte da Silva, Amauri; Veit-Acosta, Martina; Rufino Oliveira, Patricia; Tutone, Marco; Biziukova, Nadezhda; Poroikov, Vladimir; Tarasova, Olga; Baud, Stéphaine
Año de publicación
2024
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Factor de Impacto 2023: 3.4
Fil: de Azevedo, Walter Filgueira Jr. Federal University of Alfenas. Institute of Exact Sciences. Department of Physics, Brazil.
Fil: Rodrigo, Quiroga. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Departamento de Química Teórica y Computacional, Argentina.
Fil: Rodrigo, Quiroga. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Fisicoquímica de Córdoba, Argentina.
Fil: Villarreal, Marcos Ariel. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Departamento de Química Teórica y Computacional, Argentina.
Fil: Villarreal, Marcos Ariel. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Fisicoquímica de Córdoba, Argentina
Fil: Freitas da Silveira, Nelson José. Federal University of Alfenas. Laboratory of Molecular Modeling and Computer Simulation, Brazil.
Fil: Bitencourt-Ferreira, Gabriela. Pontifical Catholic University of Rio Grande do Sul, Porto Alegre, Brazil.
Fil: Duarte da Silva, Amauri. Universidade Federal de Ciências da Saúde de Porto Alegre. Programa de Pós-Graduação em Tecnologias da Informação e Gestão em Saúde, Brazil.
Fil: Veit-Acosta, Martina. Western Michigan University. Michigan, USA.
Fil: Rufino Oliveira, Patricia. University of São Paulo. School of Arts, Sciences and Humanities, Brazil.
Fil: Tutone, Marco. Università di Palermo. Dipartimento di Scienze e Tecnologie Biologiche Chimiche e Farmaceutiche, Italy.
Fil: Biziukova, Nadezhda. Institute of Biomedical Chemistry, Moscow, Russia.
Fil: Poroikov, Vladimir. Institute of Biomedical Chemistry, Moscow, Russia.
Fil: Tarasova, Olga. Institute of Biomedical Chemistry, Moscow, Russia.
Fil: Baud, Stéphaine. Université de Reims Champagne-Ardenne. Laboratoire SiRMa, UMR CNRS/URCA 7369, UFR Sciences Exactes et Naturelles, Reims, France.
Classical scoring functions may exhibit low accuracy in determining ligand binding affinity for proteins. The availability of both protein-ligand structures and affinity data make it possible to develop machine-learning models focused on specific protein systems with superior predictive performance. Here, we report a new methodology named SAnDReS that combines AutoDock Vina 1.2 with 54 regression methods available in Scikit-Learn to calculate binding affinity based on protein-ligand structures. This approach allows exploration of the scoring function space. SAnDReS generates machine-learning models based on crystal, docked, and AlphaFold-generated structures. As a proof of concept, we examine the performance of SAnDReS-generated models in three case studies. For all three cases, our models outperformed classical scoring functions. Also, SAnDReS-generated models showed predictive performance close to or better than other machine-learning models such as KDEEP, CSM-lig, and ΔVinaRF20. SAnDReS 2.0 is available to download at https://github.com/azevedolab/sandres.
info:eu-repo/semantics/publishedVersion
Fil: de Azevedo, Walter Filgueira Jr. Federal University of Alfenas. Institute of Exact Sciences. Department of Physics, Brazil.
Fil: Rodrigo, Quiroga. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Departamento de Química Teórica y Computacional, Argentina.
Fil: Rodrigo, Quiroga. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Fisicoquímica de Córdoba, Argentina.
Fil: Villarreal, Marcos Ariel. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Departamento de Química Teórica y Computacional, Argentina.
Fil: Villarreal, Marcos Ariel. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Fisicoquímica de Córdoba, Argentina
Fil: Freitas da Silveira, Nelson José. Federal University of Alfenas. Laboratory of Molecular Modeling and Computer Simulation, Brazil.
Fil: Bitencourt-Ferreira, Gabriela. Pontifical Catholic University of Rio Grande do Sul, Porto Alegre, Brazil.
Fil: Duarte da Silva, Amauri. Universidade Federal de Ciências da Saúde de Porto Alegre. Programa de Pós-Graduação em Tecnologias da Informação e Gestão em Saúde, Brazil.
Fil: Veit-Acosta, Martina. Western Michigan University. Michigan, USA.
Fil: Rufino Oliveira, Patricia. University of São Paulo. School of Arts, Sciences and Humanities, Brazil.
Fil: Tutone, Marco. Università di Palermo. Dipartimento di Scienze e Tecnologie Biologiche Chimiche e Farmaceutiche, Italy.
Fil: Biziukova, Nadezhda. Institute of Biomedical Chemistry, Moscow, Russia.
Fil: Poroikov, Vladimir. Institute of Biomedical Chemistry, Moscow, Russia.
Fil: Tarasova, Olga. Institute of Biomedical Chemistry, Moscow, Russia.
Fil: Baud, Stéphaine. Université de Reims Champagne-Ardenne. Laboratoire SiRMa, UMR CNRS/URCA 7369, UFR Sciences Exactes et Naturelles, Reims, France.
Materia
Binding affinity
Crystal structure
Machine learning
Protein–ligand interactions
Scoring function space
Nivel de accesibilidad
acceso abierto
Condiciones de uso
Repositorio
Repositorio Digital Universitario (UNC)
Institución
Universidad Nacional de Córdoba
OAI Identificador
oai:rdu.unc.edu.ar:11086/552524
Acceder
id RDUUNC_350e1bbaca32c912f6e935da71cb4e3f
oai_identifier_str oai:rdu.unc.edu.ar:11086/552524
network_acronym_str RDUUNC
repository_id_str 2572
network_name_str Repositorio Digital Universitario (UNC)
spelling SAnDReS 2.0: Development of machine-learning models to explore the scoring function spacede Azevedo, Walter Filgueira Jr.Rodrigo, QuirogaVillarreal, Marcos ArielFreitas da Silveira, Nelson JoséBitencourt-Ferreira, GabrielaDuarte da Silva, AmauriVeit-Acosta, MartinaRufino Oliveira, PatriciaTutone, MarcoBiziukova, NadezhdaPoroikov, VladimirTarasova, OlgaBaud, StéphaineBinding affinityCrystal structureMachine learningProtein–ligand interactionsScoring function spaceFactor de Impacto 2023: 3.4Fil: de Azevedo, Walter Filgueira Jr. Federal University of Alfenas. Institute of Exact Sciences. Department of Physics, Brazil.Fil: Rodrigo, Quiroga. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Departamento de Química Teórica y Computacional, Argentina.Fil: Rodrigo, Quiroga. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Fisicoquímica de Córdoba, Argentina.Fil: Villarreal, Marcos Ariel. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Departamento de Química Teórica y Computacional, Argentina.Fil: Villarreal, Marcos Ariel. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Fisicoquímica de Córdoba, ArgentinaFil: Freitas da Silveira, Nelson José. Federal University of Alfenas. Laboratory of Molecular Modeling and Computer Simulation, Brazil.Fil: Bitencourt-Ferreira, Gabriela. Pontifical Catholic University of Rio Grande do Sul, Porto Alegre, Brazil.Fil: Duarte da Silva, Amauri. Universidade Federal de Ciências da Saúde de Porto Alegre. Programa de Pós-Graduação em Tecnologias da Informação e Gestão em Saúde, Brazil.Fil: Veit-Acosta, Martina. Western Michigan University. Michigan, USA.Fil: Rufino Oliveira, Patricia. University of São Paulo. School of Arts, Sciences and Humanities, Brazil.Fil: Tutone, Marco. Università di Palermo. Dipartimento di Scienze e Tecnologie Biologiche Chimiche e Farmaceutiche, Italy.Fil: Biziukova, Nadezhda. Institute of Biomedical Chemistry, Moscow, Russia.Fil: Poroikov, Vladimir. Institute of Biomedical Chemistry, Moscow, Russia.Fil: Tarasova, Olga. Institute of Biomedical Chemistry, Moscow, Russia.Fil: Baud, Stéphaine. Université de Reims Champagne-Ardenne. Laboratoire SiRMa, UMR CNRS/URCA 7369, UFR Sciences Exactes et Naturelles, Reims, France.Classical scoring functions may exhibit low accuracy in determining ligand binding affinity for proteins. The availability of both protein-ligand structures and affinity data make it possible to develop machine-learning models focused on specific protein systems with superior predictive performance. Here, we report a new methodology named SAnDReS that combines AutoDock Vina 1.2 with 54 regression methods available in Scikit-Learn to calculate binding affinity based on protein-ligand structures. This approach allows exploration of the scoring function space. SAnDReS generates machine-learning models based on crystal, docked, and AlphaFold-generated structures. As a proof of concept, we examine the performance of SAnDReS-generated models in three case studies. For all three cases, our models outperformed classical scoring functions. Also, SAnDReS-generated models showed predictive performance close to or better than other machine-learning models such as KDEEP, CSM-lig, and ΔVinaRF20. SAnDReS 2.0 is available to download at https://github.com/azevedolab/sandres.info:eu-repo/semantics/publishedVersionFil: de Azevedo, Walter Filgueira Jr. Federal University of Alfenas. Institute of Exact Sciences. Department of Physics, Brazil.Fil: Rodrigo, Quiroga. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Departamento de Química Teórica y Computacional, Argentina.Fil: Rodrigo, Quiroga. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Fisicoquímica de Córdoba, Argentina.Fil: Villarreal, Marcos Ariel. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Departamento de Química Teórica y Computacional, Argentina.Fil: Villarreal, Marcos Ariel. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Fisicoquímica de Córdoba, ArgentinaFil: Freitas da Silveira, Nelson José. Federal University of Alfenas. Laboratory of Molecular Modeling and Computer Simulation, Brazil.Fil: Bitencourt-Ferreira, Gabriela. Pontifical Catholic University of Rio Grande do Sul, Porto Alegre, Brazil.Fil: Duarte da Silva, Amauri. Universidade Federal de Ciências da Saúde de Porto Alegre. Programa de Pós-Graduação em Tecnologias da Informação e Gestão em Saúde, Brazil.Fil: Veit-Acosta, Martina. Western Michigan University. Michigan, USA.Fil: Rufino Oliveira, Patricia. University of São Paulo. School of Arts, Sciences and Humanities, Brazil.Fil: Tutone, Marco. Università di Palermo. Dipartimento di Scienze e Tecnologie Biologiche Chimiche e Farmaceutiche, Italy.Fil: Biziukova, Nadezhda. Institute of Biomedical Chemistry, Moscow, Russia.Fil: Poroikov, Vladimir. Institute of Biomedical Chemistry, Moscow, Russia.Fil: Tarasova, Olga. Institute of Biomedical Chemistry, Moscow, Russia.Fil: Baud, Stéphaine. Université de Reims Champagne-Ardenne. Laboratoire SiRMa, UMR CNRS/URCA 7369, UFR Sciences Exactes et Naturelles, Reims, France.https://orcid.org/0000-0001-8640-357Xhttps://orcid.org/0000-0001-5015-0531https://orcid.org/0000-0001-8223-5193https://orcid.org/0000-0001-9257-7322https://orcid.org/0000-0002-3120-8256https://orcid.org/0000-0001-6395-458Xhttps://orcid.org/0000-0002-9203-3314https://orcid.org/0000-0002-1850-6670https://orcid.org/0000-0001-5059-8686https://orcid.org/0000-0002-2044-1327https://orcid.org/0000-0001-7937-2621https://orcid.org/0000-0002-3723-78322024-06-20info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfde Azevedo Jr, W. F., Quiroga, R., Villarreal, M. A., da Silveira, N. J. F., Bitencourt‐Ferreira, G., da Silva, A. D., ... & Baud, S. SAnDReS 2.0: Development of machine‐learning models to explore the scoring function space. Journal of Computational Chemistry.http://hdl.handle.net/11086/5525241096-987Xhttps://onlinelibrary.wiley.com/doi/abs/10.1002/jcc.27449https://pubmed.ncbi.nlm.nih.gov/38900052/DOI: 10.1002/jcc.27449enginfo:eu-repo/semantics/openAccessreponame:Repositorio Digital Universitario (UNC)instname:Universidad Nacional de Córdobainstacron:UNC2025-09-29T13:41:16Zoai:rdu.unc.edu.ar:11086/552524Institucionalhttps://rdu.unc.edu.ar/Universidad públicaNo correspondehttp://rdu.unc.edu.ar/oai/snrdoca.unc@gmail.comArgentinaNo correspondeNo correspondeNo correspondeopendoar:25722025-09-29 13:41:16.962Repositorio Digital Universitario (UNC) - Universidad Nacional de Córdobafalse
dc.title.none.fl_str_mv SAnDReS 2.0: Development of machine-learning models to explore the scoring function space
title SAnDReS 2.0: Development of machine-learning models to explore the scoring function space
spellingShingle SAnDReS 2.0: Development of machine-learning models to explore the scoring function space
de Azevedo, Walter Filgueira Jr.
Binding affinity
Crystal structure
Machine learning
Protein–ligand interactions
Scoring function space
title_short SAnDReS 2.0: Development of machine-learning models to explore the scoring function space
title_full SAnDReS 2.0: Development of machine-learning models to explore the scoring function space
title_fullStr SAnDReS 2.0: Development of machine-learning models to explore the scoring function space
title_full_unstemmed SAnDReS 2.0: Development of machine-learning models to explore the scoring function space
title_sort SAnDReS 2.0: Development of machine-learning models to explore the scoring function space
dc.creator.none.fl_str_mv de Azevedo, Walter Filgueira Jr.
Rodrigo, Quiroga
Villarreal, Marcos Ariel
Freitas da Silveira, Nelson José
Bitencourt-Ferreira, Gabriela
Duarte da Silva, Amauri
Veit-Acosta, Martina
Rufino Oliveira, Patricia
Tutone, Marco
Biziukova, Nadezhda
Poroikov, Vladimir
Tarasova, Olga
Baud, Stéphaine
author de Azevedo, Walter Filgueira Jr.
author_facet de Azevedo, Walter Filgueira Jr.
Rodrigo, Quiroga
Villarreal, Marcos Ariel
Freitas da Silveira, Nelson José
Bitencourt-Ferreira, Gabriela
Duarte da Silva, Amauri
Veit-Acosta, Martina
Rufino Oliveira, Patricia
Tutone, Marco
Biziukova, Nadezhda
Poroikov, Vladimir
Tarasova, Olga
Baud, Stéphaine
author_role author
author2 Rodrigo, Quiroga
Villarreal, Marcos Ariel
Freitas da Silveira, Nelson José
Bitencourt-Ferreira, Gabriela
Duarte da Silva, Amauri
Veit-Acosta, Martina
Rufino Oliveira, Patricia
Tutone, Marco
Biziukova, Nadezhda
Poroikov, Vladimir
Tarasova, Olga
Baud, Stéphaine
author2_role author
author
author
author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv https://orcid.org/0000-0001-8640-357X
https://orcid.org/0000-0001-5015-0531
https://orcid.org/0000-0001-8223-5193
https://orcid.org/0000-0001-9257-7322
https://orcid.org/0000-0002-3120-8256
https://orcid.org/0000-0001-6395-458X
https://orcid.org/0000-0002-9203-3314
https://orcid.org/0000-0002-1850-6670
https://orcid.org/0000-0001-5059-8686
https://orcid.org/0000-0002-2044-1327
https://orcid.org/0000-0001-7937-2621
https://orcid.org/0000-0002-3723-7832
dc.subject.none.fl_str_mv Binding affinity
Crystal structure
Machine learning
Protein–ligand interactions
Scoring function space
topic Binding affinity
Crystal structure
Machine learning
Protein–ligand interactions
Scoring function space
dc.description.none.fl_txt_mv Factor de Impacto 2023: 3.4
Fil: de Azevedo, Walter Filgueira Jr. Federal University of Alfenas. Institute of Exact Sciences. Department of Physics, Brazil.
Fil: Rodrigo, Quiroga. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Departamento de Química Teórica y Computacional, Argentina.
Fil: Rodrigo, Quiroga. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Fisicoquímica de Córdoba, Argentina.
Fil: Villarreal, Marcos Ariel. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Departamento de Química Teórica y Computacional, Argentina.
Fil: Villarreal, Marcos Ariel. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Fisicoquímica de Córdoba, Argentina
Fil: Freitas da Silveira, Nelson José. Federal University of Alfenas. Laboratory of Molecular Modeling and Computer Simulation, Brazil.
Fil: Bitencourt-Ferreira, Gabriela. Pontifical Catholic University of Rio Grande do Sul, Porto Alegre, Brazil.
Fil: Duarte da Silva, Amauri. Universidade Federal de Ciências da Saúde de Porto Alegre. Programa de Pós-Graduação em Tecnologias da Informação e Gestão em Saúde, Brazil.
Fil: Veit-Acosta, Martina. Western Michigan University. Michigan, USA.
Fil: Rufino Oliveira, Patricia. University of São Paulo. School of Arts, Sciences and Humanities, Brazil.
Fil: Tutone, Marco. Università di Palermo. Dipartimento di Scienze e Tecnologie Biologiche Chimiche e Farmaceutiche, Italy.
Fil: Biziukova, Nadezhda. Institute of Biomedical Chemistry, Moscow, Russia.
Fil: Poroikov, Vladimir. Institute of Biomedical Chemistry, Moscow, Russia.
Fil: Tarasova, Olga. Institute of Biomedical Chemistry, Moscow, Russia.
Fil: Baud, Stéphaine. Université de Reims Champagne-Ardenne. Laboratoire SiRMa, UMR CNRS/URCA 7369, UFR Sciences Exactes et Naturelles, Reims, France.
Classical scoring functions may exhibit low accuracy in determining ligand binding affinity for proteins. The availability of both protein-ligand structures and affinity data make it possible to develop machine-learning models focused on specific protein systems with superior predictive performance. Here, we report a new methodology named SAnDReS that combines AutoDock Vina 1.2 with 54 regression methods available in Scikit-Learn to calculate binding affinity based on protein-ligand structures. This approach allows exploration of the scoring function space. SAnDReS generates machine-learning models based on crystal, docked, and AlphaFold-generated structures. As a proof of concept, we examine the performance of SAnDReS-generated models in three case studies. For all three cases, our models outperformed classical scoring functions. Also, SAnDReS-generated models showed predictive performance close to or better than other machine-learning models such as KDEEP, CSM-lig, and ΔVinaRF20. SAnDReS 2.0 is available to download at https://github.com/azevedolab/sandres.
info:eu-repo/semantics/publishedVersion
Fil: de Azevedo, Walter Filgueira Jr. Federal University of Alfenas. Institute of Exact Sciences. Department of Physics, Brazil.
Fil: Rodrigo, Quiroga. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Departamento de Química Teórica y Computacional, Argentina.
Fil: Rodrigo, Quiroga. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Fisicoquímica de Córdoba, Argentina.
Fil: Villarreal, Marcos Ariel. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Departamento de Química Teórica y Computacional, Argentina.
Fil: Villarreal, Marcos Ariel. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Fisicoquímica de Córdoba, Argentina
Fil: Freitas da Silveira, Nelson José. Federal University of Alfenas. Laboratory of Molecular Modeling and Computer Simulation, Brazil.
Fil: Bitencourt-Ferreira, Gabriela. Pontifical Catholic University of Rio Grande do Sul, Porto Alegre, Brazil.
Fil: Duarte da Silva, Amauri. Universidade Federal de Ciências da Saúde de Porto Alegre. Programa de Pós-Graduação em Tecnologias da Informação e Gestão em Saúde, Brazil.
Fil: Veit-Acosta, Martina. Western Michigan University. Michigan, USA.
Fil: Rufino Oliveira, Patricia. University of São Paulo. School of Arts, Sciences and Humanities, Brazil.
Fil: Tutone, Marco. Università di Palermo. Dipartimento di Scienze e Tecnologie Biologiche Chimiche e Farmaceutiche, Italy.
Fil: Biziukova, Nadezhda. Institute of Biomedical Chemistry, Moscow, Russia.
Fil: Poroikov, Vladimir. Institute of Biomedical Chemistry, Moscow, Russia.
Fil: Tarasova, Olga. Institute of Biomedical Chemistry, Moscow, Russia.
Fil: Baud, Stéphaine. Université de Reims Champagne-Ardenne. Laboratoire SiRMa, UMR CNRS/URCA 7369, UFR Sciences Exactes et Naturelles, Reims, France.
description Factor de Impacto 2023: 3.4
publishDate 2024
dc.date.none.fl_str_mv 2024-06-20
dc.type.none.fl_str_mv info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
status_str publishedVersion
format article
dc.identifier.none.fl_str_mv de Azevedo Jr, W. F., Quiroga, R., Villarreal, M. A., da Silveira, N. J. F., Bitencourt‐Ferreira, G., da Silva, A. D., ... & Baud, S. SAnDReS 2.0: Development of machine‐learning models to explore the scoring function space. Journal of Computational Chemistry.
http://hdl.handle.net/11086/552524
1096-987X
https://onlinelibrary.wiley.com/doi/abs/10.1002/jcc.27449
https://pubmed.ncbi.nlm.nih.gov/38900052/
DOI: 10.1002/jcc.27449
identifier_str_mv de Azevedo Jr, W. F., Quiroga, R., Villarreal, M. A., da Silveira, N. J. F., Bitencourt‐Ferreira, G., da Silva, A. D., ... & Baud, S. SAnDReS 2.0: Development of machine‐learning models to explore the scoring function space. Journal of Computational Chemistry.
1096-987X
DOI: 10.1002/jcc.27449
url http://hdl.handle.net/11086/552524
https://onlinelibrary.wiley.com/doi/abs/10.1002/jcc.27449
https://pubmed.ncbi.nlm.nih.gov/38900052/
dc.language.none.fl_str_mv eng
language eng
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.source.none.fl_str_mv reponame:Repositorio Digital Universitario (UNC)
instname:Universidad Nacional de Córdoba
instacron:UNC
reponame_str Repositorio Digital Universitario (UNC)
collection Repositorio Digital Universitario (UNC)
instname_str Universidad Nacional de Córdoba
instacron_str UNC
institution UNC
repository.name.fl_str_mv Repositorio Digital Universitario (UNC) - Universidad Nacional de Córdoba
repository.mail.fl_str_mv oca.unc@gmail.com
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score 13.070432

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