A machine learning-based virtual screening for natural compounds capable of inhibiting the HIV-1 integrase

Detalhes bibliográficos
Autor(a) principal: Machado, Lucas A.
Data de Publicação: 2022
Outros Autores: Krempser, Eduardo, Guimarães, Ana Carolina Ramos
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da FIOCRUZ (ARCA)
Texto Completo: https://www.arca.fiocruz.br/handle/icict/56334
Resumo: Faculty of Exact and Natural Sciences, University of Buenos Aires. Buenos Aires, Argentina.
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spelling Machado, Lucas A.Krempser, EduardoGuimarães, Ana Carolina Ramos2023-01-05T18:31:28Z2023-01-05T18:31:28Z2022MACHADO, Lucas A.; KREMPSER, Eduardo; GUIMARÃES, Ana Carolina Ramos. A machine learning-based virtual screening for natural compounds capable of inhibiting the HIV-1 integrase. Frontiers in Drug Discovery. v.2, 954911, p. 1 - 13, Oct. 2022.2674-0338https://www.arca.fiocruz.br/handle/icict/5633410.3389/fddsv.2022.954911engFrontiers MediaAprendizado de máquinaHIV-1IntegrarCompostos naturaisInibiçãoMachine learningHIV-1IntegraseNatural compoundsInhibitionA machine learning-based virtual screening for natural compounds capable of inhibiting the HIV-1 integraseinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleFaculty of Exact and Natural Sciences, University of Buenos Aires. Buenos Aires, Argentina.Fundação Oswaldo Cruz. Plataforma Institucional para a Biodiversidade e Saúde da Vida Selvagem. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Genômica Funcional e Bioinformática. Rio de Janeiro, RJ, Brasil.HIV-1 integrase is an essential enzyme for the HIV-1 replication cycle, and currently, integrase inhibitors are in the first line of treatment in many guidelines. Despite the discovery of new inhibitors, including a new class of molecules with different mechanisms of action, resistance is still a relevant problem, and adding new options to the therapeutic arsenal to fight viral resistance is a Sisyphean task. Because of the difficulty and cost of in vitro screenings, machine learningdriven ligand-based virtual screenings are an alternative that can not only cut costs but also use valuable information about active compounds with yet unknown mechanisms of action. In this work, we describe a thorough model exploration and hyperparameter tuning procedure in a dataset with class imbalance and show several models capable of distinguishing between compounds that are active or inactive against the HIV-1 integrase. The best of the models was then used to screen the natural product atlas for active compounds, resulting in a myriad of molecules that share features with known integrase inhibitors. Here we also explore the strengths and shortcomings of our models and discuss the use of the applicability domain to guide in vitro screenings and differentiate between the “predictable” and “unknown” regions of the chemical space.info:eu-repo/semantics/openAccessreponame:Repositório Institucional da FIOCRUZ (ARCA)instname:Fundação Oswaldo Cruz (FIOCRUZ)instacron:FIOCRUZLICENSElicense.txtlicense.txttext/plain; charset=utf-82991https://www.arca.fiocruz.br/bitstream/icict/56334/1/license.txt5a560609d32a3863062d77ff32785d58MD51ORIGINALAnaCarolinaGuimarães_etal_IOC_2022 - Copia.pdfAnaCarolinaGuimarães_etal_IOC_2022 - Copia.pdfapplication/pdf2475107https://www.arca.fiocruz.br/bitstream/icict/56334/2/AnaCarolinaGuimar%c3%a3es_etal_IOC_2022%20-%20Copia.pdf0dbd97fc4d026ea9fcf7b55df6de2158MD52icict/563342023-09-04 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dc.title.en_US.fl_str_mv A machine learning-based virtual screening for natural compounds capable of inhibiting the HIV-1 integrase
title A machine learning-based virtual screening for natural compounds capable of inhibiting the HIV-1 integrase
spellingShingle A machine learning-based virtual screening for natural compounds capable of inhibiting the HIV-1 integrase
Machado, Lucas A.
Aprendizado de máquina
HIV-1
Integrar
Compostos naturais
Inibição
Machine learning
HIV-1
Integrase
Natural compounds
Inhibition
title_short A machine learning-based virtual screening for natural compounds capable of inhibiting the HIV-1 integrase
title_full A machine learning-based virtual screening for natural compounds capable of inhibiting the HIV-1 integrase
title_fullStr A machine learning-based virtual screening for natural compounds capable of inhibiting the HIV-1 integrase
title_full_unstemmed A machine learning-based virtual screening for natural compounds capable of inhibiting the HIV-1 integrase
title_sort A machine learning-based virtual screening for natural compounds capable of inhibiting the HIV-1 integrase
author Machado, Lucas A.
author_facet Machado, Lucas A.
Krempser, Eduardo
Guimarães, Ana Carolina Ramos
author_role author
author2 Krempser, Eduardo
Guimarães, Ana Carolina Ramos
author2_role author
author
dc.contributor.author.fl_str_mv Machado, Lucas A.
Krempser, Eduardo
Guimarães, Ana Carolina Ramos
dc.subject.other.en_US.fl_str_mv Aprendizado de máquina
HIV-1
Integrar
Compostos naturais
Inibição
topic Aprendizado de máquina
HIV-1
Integrar
Compostos naturais
Inibição
Machine learning
HIV-1
Integrase
Natural compounds
Inhibition
dc.subject.en.en_US.fl_str_mv Machine learning
HIV-1
Integrase
Natural compounds
Inhibition
description Faculty of Exact and Natural Sciences, University of Buenos Aires. Buenos Aires, Argentina.
publishDate 2022
dc.date.issued.fl_str_mv 2022
dc.date.accessioned.fl_str_mv 2023-01-05T18:31:28Z
dc.date.available.fl_str_mv 2023-01-05T18:31:28Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.citation.fl_str_mv MACHADO, Lucas A.; KREMPSER, Eduardo; GUIMARÃES, Ana Carolina Ramos. A machine learning-based virtual screening for natural compounds capable of inhibiting the HIV-1 integrase. Frontiers in Drug Discovery. v.2, 954911, p. 1 - 13, Oct. 2022.
dc.identifier.uri.fl_str_mv https://www.arca.fiocruz.br/handle/icict/56334
dc.identifier.issn.en_US.fl_str_mv 2674-0338
dc.identifier.doi.none.fl_str_mv 10.3389/fddsv.2022.954911
identifier_str_mv MACHADO, Lucas A.; KREMPSER, Eduardo; GUIMARÃES, Ana Carolina Ramos. A machine learning-based virtual screening for natural compounds capable of inhibiting the HIV-1 integrase. Frontiers in Drug Discovery. v.2, 954911, p. 1 - 13, Oct. 2022.
2674-0338
10.3389/fddsv.2022.954911
url https://www.arca.fiocruz.br/handle/icict/56334
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Frontiers Media
publisher.none.fl_str_mv Frontiers Media
dc.source.none.fl_str_mv reponame:Repositório Institucional da FIOCRUZ (ARCA)
instname:Fundação Oswaldo Cruz (FIOCRUZ)
instacron:FIOCRUZ
instname_str Fundação Oswaldo Cruz (FIOCRUZ)
instacron_str FIOCRUZ
institution FIOCRUZ
reponame_str Repositório Institucional da FIOCRUZ (ARCA)
collection Repositório Institucional da FIOCRUZ (ARCA)
bitstream.url.fl_str_mv https://www.arca.fiocruz.br/bitstream/icict/56334/1/license.txt
https://www.arca.fiocruz.br/bitstream/icict/56334/2/AnaCarolinaGuimar%c3%a3es_etal_IOC_2022%20-%20Copia.pdf
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repository.name.fl_str_mv Repositório Institucional da FIOCRUZ (ARCA) - Fundação Oswaldo Cruz (FIOCRUZ)
repository.mail.fl_str_mv repositorio.arca@fiocruz.br
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