Ensemble learning application to discover new trypanothione synthetase inhibitors

Autores
Alice, Juan Ignacio; Bellera, Carolina Leticia; Benítez Boné, Diego Raúl; Comini, Marcelo A.; Duchowicz, Pablo Román; Talevi, Alan
Año de publicación
2021
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Trypanosomatid-caused diseases are among the neglected infectious diseases with the highest disease burden, affecting about 27 million people worldwide and, in particular, socio-economically vulnerable populations. Trypanothione synthetase (TryS) is considered one of the most attractive drug targets within the thiol-polyamine metabolism of typanosomatids, being unique, essential and druggable. Here, we have compiled a dataset of 401 T. brucei TryS inhibitors that includes compounds with inhibitory data reported in the literature, but also in-house acquired data. QSAR classifiers were derived and validated from such dataset, using publicly available and open-source software, thus assuring the portability of the obtained models. The performance and robustness of the resulting models were substantially improved through ensemble learning. The performance of the individual models and the model ensembles was further assessed through retrospective virtual screening campaigns. At last, as an application example, the chosen model-ensemble has been applied in a prospective virtual screening campaign on DrugBank 5.1.6 compound library. All the in-house scripts used in this study are available on request, whereas the dataset has been included as supplementary material.
Facultad de Ciencias Exactas
Laboratorio de Investigación y Desarrollo de Bioactivos
Materia
Ciencias Exactas
Medicina
Ensemble learning
Machine learning
QSAR
Trypanosoma cruzi
Chagas disease
Trypanothione synthetase
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/133709

id SEDICI_5a42aed843aa761b45730edea627bfcf
oai_identifier_str oai:sedici.unlp.edu.ar:10915/133709
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Ensemble learning application to discover new trypanothione synthetase inhibitorsAlice, Juan IgnacioBellera, Carolina LeticiaBenítez Boné, Diego RaúlComini, Marcelo A.Duchowicz, Pablo RománTalevi, AlanCiencias ExactasMedicinaEnsemble learningMachine learningQSARTrypanosoma cruziChagas diseaseTrypanothione synthetaseTrypanosomatid-caused diseases are among the neglected infectious diseases with the highest disease burden, affecting about 27 million people worldwide and, in particular, socio-economically vulnerable populations. Trypanothione synthetase (TryS) is considered one of the most attractive drug targets within the thiol-polyamine metabolism of typanosomatids, being unique, essential and druggable. Here, we have compiled a dataset of 401 T. brucei TryS inhibitors that includes compounds with inhibitory data reported in the literature, but also in-house acquired data. QSAR classifiers were derived and validated from such dataset, using publicly available and open-source software, thus assuring the portability of the obtained models. The performance and robustness of the resulting models were substantially improved through ensemble learning. The performance of the individual models and the model ensembles was further assessed through retrospective virtual screening campaigns. At last, as an application example, the chosen model-ensemble has been applied in a prospective virtual screening campaign on DrugBank 5.1.6 compound library. All the in-house scripts used in this study are available on request, whereas the dataset has been included as supplementary material.Facultad de Ciencias ExactasLaboratorio de Investigación y Desarrollo de Bioactivos2021-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdf1361-1373http://sedici.unlp.edu.ar/handle/10915/133709enginfo:eu-repo/semantics/altIdentifier/issn/1573-501Xinfo:eu-repo/semantics/altIdentifier/issn/1381-1991info:eu-repo/semantics/altIdentifier/doi/10.1007/s11030-021-10265-9info:eu-repo/semantics/altIdentifier/pmid/34264440info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International (CC BY 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:31:53Zoai:sedici.unlp.edu.ar:10915/133709Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:31:53.307SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Ensemble learning application to discover new trypanothione synthetase inhibitors
title Ensemble learning application to discover new trypanothione synthetase inhibitors
spellingShingle Ensemble learning application to discover new trypanothione synthetase inhibitors
Alice, Juan Ignacio
Ciencias Exactas
Medicina
Ensemble learning
Machine learning
QSAR
Trypanosoma cruzi
Chagas disease
Trypanothione synthetase
title_short Ensemble learning application to discover new trypanothione synthetase inhibitors
title_full Ensemble learning application to discover new trypanothione synthetase inhibitors
title_fullStr Ensemble learning application to discover new trypanothione synthetase inhibitors
title_full_unstemmed Ensemble learning application to discover new trypanothione synthetase inhibitors
title_sort Ensemble learning application to discover new trypanothione synthetase inhibitors
dc.creator.none.fl_str_mv Alice, Juan Ignacio
Bellera, Carolina Leticia
Benítez Boné, Diego Raúl
Comini, Marcelo A.
Duchowicz, Pablo Román
Talevi, Alan
author Alice, Juan Ignacio
author_facet Alice, Juan Ignacio
Bellera, Carolina Leticia
Benítez Boné, Diego Raúl
Comini, Marcelo A.
Duchowicz, Pablo Román
Talevi, Alan
author_role author
author2 Bellera, Carolina Leticia
Benítez Boné, Diego Raúl
Comini, Marcelo A.
Duchowicz, Pablo Román
Talevi, Alan
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Ciencias Exactas
Medicina
Ensemble learning
Machine learning
QSAR
Trypanosoma cruzi
Chagas disease
Trypanothione synthetase
topic Ciencias Exactas
Medicina
Ensemble learning
Machine learning
QSAR
Trypanosoma cruzi
Chagas disease
Trypanothione synthetase
dc.description.none.fl_txt_mv Trypanosomatid-caused diseases are among the neglected infectious diseases with the highest disease burden, affecting about 27 million people worldwide and, in particular, socio-economically vulnerable populations. Trypanothione synthetase (TryS) is considered one of the most attractive drug targets within the thiol-polyamine metabolism of typanosomatids, being unique, essential and druggable. Here, we have compiled a dataset of 401 T. brucei TryS inhibitors that includes compounds with inhibitory data reported in the literature, but also in-house acquired data. QSAR classifiers were derived and validated from such dataset, using publicly available and open-source software, thus assuring the portability of the obtained models. The performance and robustness of the resulting models were substantially improved through ensemble learning. The performance of the individual models and the model ensembles was further assessed through retrospective virtual screening campaigns. At last, as an application example, the chosen model-ensemble has been applied in a prospective virtual screening campaign on DrugBank 5.1.6 compound library. All the in-house scripts used in this study are available on request, whereas the dataset has been included as supplementary material.
Facultad de Ciencias Exactas
Laboratorio de Investigación y Desarrollo de Bioactivos
description Trypanosomatid-caused diseases are among the neglected infectious diseases with the highest disease burden, affecting about 27 million people worldwide and, in particular, socio-economically vulnerable populations. Trypanothione synthetase (TryS) is considered one of the most attractive drug targets within the thiol-polyamine metabolism of typanosomatids, being unique, essential and druggable. Here, we have compiled a dataset of 401 T. brucei TryS inhibitors that includes compounds with inhibitory data reported in the literature, but also in-house acquired data. QSAR classifiers were derived and validated from such dataset, using publicly available and open-source software, thus assuring the portability of the obtained models. The performance and robustness of the resulting models were substantially improved through ensemble learning. The performance of the individual models and the model ensembles was further assessed through retrospective virtual screening campaigns. At last, as an application example, the chosen model-ensemble has been applied in a prospective virtual screening campaign on DrugBank 5.1.6 compound library. All the in-house scripts used in this study are available on request, whereas the dataset has been included as supplementary material.
publishDate 2021
dc.date.none.fl_str_mv 2021-08
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Articulo
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://sedici.unlp.edu.ar/handle/10915/133709
url http://sedici.unlp.edu.ar/handle/10915/133709
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/issn/1573-501X
info:eu-repo/semantics/altIdentifier/issn/1381-1991
info:eu-repo/semantics/altIdentifier/doi/10.1007/s11030-021-10265-9
info:eu-repo/semantics/altIdentifier/pmid/34264440
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/4.0/
Creative Commons Attribution 4.0 International (CC BY 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
Creative Commons Attribution 4.0 International (CC BY 4.0)
dc.format.none.fl_str_mv application/pdf
1361-1373
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
_version_ 1844616198674710528
score 13.070432