3D-QSARpy: Combining variable selection strategies and machine learning techniques to build QSAR models
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Brazilian Journal of Pharmaceutical Sciences |
Texto Completo: | https://www.revistas.usp.br/bjps/article/view/212200 |
Resumo: | Quantitative Structure-Activity Relationship (QSAR) is a computer-aided technology in the field of medicinal chemistry that seeks to clarify the relationships between molecular structures and their biological activities. Such technologies allow for the acceleration of the development of new compounds by reducing the costs of drug design. This work presents 3D-QSARpy, a flexible, user-friendly and robust tool, freely available without registration, to support the generation of QSAR 3D models in an automated way. The user only needs to provide aligned molecular structures and the respective dependent variable. The current version was developed using Python with packages such as scikit-learn and includes various techniques of machine learning for regression. The diverse techniques employed by the tool is a differential compared to known methodologies, such as CoMFA and CoMSIA, because it expands the search space of possible solutions, and in this way increases the chances of obtaining relevant models. Additionally, approaches for select variables (dimension reduction) were implemented in the tool. To evaluate its potentials, experiments were carried out to compare results obtained from the proposed 3D-QSARpy tool with the results from already published works. The results demonstrated that 3D-QSARpy is extremely useful in the field due to its expressive results. |
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Brazilian Journal of Pharmaceutical Sciences |
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3D-QSARpy: Combining variable selection strategies and machine learning techniques to build QSAR modelsDrug Design3D-QSAR; Machine learningVariable selectionQuantitative Structure-Activity Relationship (QSAR) is a computer-aided technology in the field of medicinal chemistry that seeks to clarify the relationships between molecular structures and their biological activities. Such technologies allow for the acceleration of the development of new compounds by reducing the costs of drug design. This work presents 3D-QSARpy, a flexible, user-friendly and robust tool, freely available without registration, to support the generation of QSAR 3D models in an automated way. The user only needs to provide aligned molecular structures and the respective dependent variable. The current version was developed using Python with packages such as scikit-learn and includes various techniques of machine learning for regression. The diverse techniques employed by the tool is a differential compared to known methodologies, such as CoMFA and CoMSIA, because it expands the search space of possible solutions, and in this way increases the chances of obtaining relevant models. Additionally, approaches for select variables (dimension reduction) were implemented in the tool. To evaluate its potentials, experiments were carried out to compare results obtained from the proposed 3D-QSARpy tool with the results from already published works. The results demonstrated that 3D-QSARpy is extremely useful in the field due to its expressive results.Universidade de São Paulo. Faculdade de Ciências Farmacêuticas2023-05-19info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://www.revistas.usp.br/bjps/article/view/21220010.1590/s2175-97902023e22373Brazilian Journal of Pharmaceutical Sciences; Vol. 59 (2023)Brazilian Journal of Pharmaceutical Sciences; v. 59 (2023)Brazilian Journal of Pharmaceutical Sciences; Vol. 59 (2023)2175-97901984-8250reponame:Brazilian Journal of Pharmaceutical Sciencesinstname:Universidade de São Paulo (USP)instacron:USPenghttps://www.revistas.usp.br/bjps/article/view/212200/194316Copyright (c) 2023 Brazilian Journal of Pharmaceutical Scienceshttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccess Silverio, Priscilla S. S. N.Viana, Jéssika de OliveiraGuimarães, Euzébio B.2023-05-19T17:43:50Zoai:revistas.usp.br:article/212200Revistahttps://www.revistas.usp.br/bjps/indexPUBhttps://old.scielo.br/oai/scielo-oai.phpbjps@usp.br||elizabeth.igne@gmail.com2175-97901984-8250opendoar:2023-05-19T17:43:50Brazilian Journal of Pharmaceutical Sciences - Universidade de São Paulo (USP)false |
dc.title.none.fl_str_mv |
3D-QSARpy: Combining variable selection strategies and machine learning techniques to build QSAR models |
title |
3D-QSARpy: Combining variable selection strategies and machine learning techniques to build QSAR models |
spellingShingle |
3D-QSARpy: Combining variable selection strategies and machine learning techniques to build QSAR models Silverio, Priscilla S. S. N. Drug Design 3D-QSAR; Machine learning Variable selection |
title_short |
3D-QSARpy: Combining variable selection strategies and machine learning techniques to build QSAR models |
title_full |
3D-QSARpy: Combining variable selection strategies and machine learning techniques to build QSAR models |
title_fullStr |
3D-QSARpy: Combining variable selection strategies and machine learning techniques to build QSAR models |
title_full_unstemmed |
3D-QSARpy: Combining variable selection strategies and machine learning techniques to build QSAR models |
title_sort |
3D-QSARpy: Combining variable selection strategies and machine learning techniques to build QSAR models |
author |
Silverio, Priscilla S. S. N. |
author_facet |
Silverio, Priscilla S. S. N. Viana, Jéssika de Oliveira Guimarães, Euzébio B. |
author_role |
author |
author2 |
Viana, Jéssika de Oliveira Guimarães, Euzébio B. |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Silverio, Priscilla S. S. N. Viana, Jéssika de Oliveira Guimarães, Euzébio B. |
dc.subject.por.fl_str_mv |
Drug Design 3D-QSAR; Machine learning Variable selection |
topic |
Drug Design 3D-QSAR; Machine learning Variable selection |
description |
Quantitative Structure-Activity Relationship (QSAR) is a computer-aided technology in the field of medicinal chemistry that seeks to clarify the relationships between molecular structures and their biological activities. Such technologies allow for the acceleration of the development of new compounds by reducing the costs of drug design. This work presents 3D-QSARpy, a flexible, user-friendly and robust tool, freely available without registration, to support the generation of QSAR 3D models in an automated way. The user only needs to provide aligned molecular structures and the respective dependent variable. The current version was developed using Python with packages such as scikit-learn and includes various techniques of machine learning for regression. The diverse techniques employed by the tool is a differential compared to known methodologies, such as CoMFA and CoMSIA, because it expands the search space of possible solutions, and in this way increases the chances of obtaining relevant models. Additionally, approaches for select variables (dimension reduction) were implemented in the tool. To evaluate its potentials, experiments were carried out to compare results obtained from the proposed 3D-QSARpy tool with the results from already published works. The results demonstrated that 3D-QSARpy is extremely useful in the field due to its expressive results. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-05-19 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://www.revistas.usp.br/bjps/article/view/212200 10.1590/s2175-97902023e22373 |
url |
https://www.revistas.usp.br/bjps/article/view/212200 |
identifier_str_mv |
10.1590/s2175-97902023e22373 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://www.revistas.usp.br/bjps/article/view/212200/194316 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2023 Brazilian Journal of Pharmaceutical Sciences https://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2023 Brazilian Journal of Pharmaceutical Sciences https://creativecommons.org/licenses/by/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade de São Paulo. Faculdade de Ciências Farmacêuticas |
publisher.none.fl_str_mv |
Universidade de São Paulo. Faculdade de Ciências Farmacêuticas |
dc.source.none.fl_str_mv |
Brazilian Journal of Pharmaceutical Sciences; Vol. 59 (2023) Brazilian Journal of Pharmaceutical Sciences; v. 59 (2023) Brazilian Journal of Pharmaceutical Sciences; Vol. 59 (2023) 2175-9790 1984-8250 reponame:Brazilian Journal of Pharmaceutical Sciences instname:Universidade de São Paulo (USP) instacron:USP |
instname_str |
Universidade de São Paulo (USP) |
instacron_str |
USP |
institution |
USP |
reponame_str |
Brazilian Journal of Pharmaceutical Sciences |
collection |
Brazilian Journal of Pharmaceutical Sciences |
repository.name.fl_str_mv |
Brazilian Journal of Pharmaceutical Sciences - Universidade de São Paulo (USP) |
repository.mail.fl_str_mv |
bjps@usp.br||elizabeth.igne@gmail.com |
_version_ |
1800222918104317952 |