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

Factor de Impacto 2023: 3.4

Guardado en:
Detalles Bibliográficos
Autores principales: 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
Otros Autores: https://orcid.org/0000-0001-8640-357X
Formato: publishedVersion article
Lenguaje:Inglés
Publicado: 2024
Materias:
Binding affinity
Crystal structure
Machine learning
Protein–ligand interactions
Scoring function space
Acceso en línea:http://hdl.handle.net/11086/552524
https://onlinelibrary.wiley.com/doi/abs/10.1002/jcc.27449
https://pubmed.ncbi.nlm.nih.gov/38900052/
Aporte de:
Repositorio Digital Universitario (UNC) de Universidad Nacional de Córdoba
id I10-R141-11086-552524
record_format dspace
institution Universidad Nacional de Córdoba
institution_str I-10
repository_str R-141
collection Repositorio Digital Universitario (UNC)
language Inglés
topic Binding affinity
Crystal structure
Machine learning
Protein–ligand interactions
Scoring function space
spellingShingle Binding affinity
Crystal structure
Machine learning
Protein–ligand interactions
Scoring function space
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
SAnDReS 2.0: Development of machine-learning models to explore the scoring function space
topic_facet Binding affinity
Crystal structure
Machine learning
Protein–ligand interactions
Scoring function space
description Factor de Impacto 2023: 3.4
author2 https://orcid.org/0000-0001-8640-357X
author_facet https://orcid.org/0000-0001-8640-357X
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
format publishedVersion
article
author 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_sort de Azevedo, Walter Filgueira Jr.
title SAnDReS 2.0: Development of machine-learning models to explore the 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
publishDate 2024
url http://hdl.handle.net/11086/552524
https://onlinelibrary.wiley.com/doi/abs/10.1002/jcc.27449
https://pubmed.ncbi.nlm.nih.gov/38900052/
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spelling I10-R141-11086-5525242024-07-03T06:24:03Z SAnDReS 2.0: Development of machine-learning models to explore the scoring function space 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 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 Binding affinity Crystal structure Machine learning Protein–ligand interactions Scoring function space Factor de Impacto 2023: 3.4 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. 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. 2024-07-02T22:32:18Z 2024-07-02T22:32:18Z 2024-06-20 article 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 eng Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/

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