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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/133709
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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 |
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http://sedici.unlp.edu.ar/handle/10915/133709 |
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http://sedici.unlp.edu.ar/handle/10915/133709 |
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eng |
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eng |
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openAccess |
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http://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International (CC BY 4.0) |
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