Ensemble learning application to discover new trypanothione synthetase inhibitors

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...

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Detalles Bibliográficos
Autores principales:	Alice, Juan Ignacio, Bellera, Carolina Leticia, Benítez Boné, Diego Raúl, Comini, Marcelo A., Duchowicz, Pablo Román, Talevi, Alan
Formato:	Articulo
Lenguaje:	Inglés
Publicado:	2021
Materias:	Ciencias Exactas Medicina Ensemble learning Machine learning QSAR Trypanosoma cruzi Chagas disease Trypanothione synthetase
Acceso en línea:	http://sedici.unlp.edu.ar/handle/10915/133709
Aporte de:	SEDICI (UNLP) de Universidad Nacional de La Plata

id	I19-R120-10915-133709
record_format	dspace
institution	Universidad Nacional de La Plata
institution_str	I-19
repository_str	R-120
collection	SEDICI (UNLP)
language	Inglés
topic	Ciencias Exactas Medicina Ensemble learning Machine learning QSAR Trypanosoma cruzi Chagas disease Trypanothione synthetase
spellingShingle	Ciencias Exactas Medicina Ensemble learning Machine learning QSAR Trypanosoma cruzi Chagas disease Trypanothione synthetase Alice, Juan Ignacio Bellera, Carolina Leticia Benítez Boné, Diego Raúl Comini, Marcelo A. Duchowicz, Pablo Román Talevi, Alan Ensemble learning application to discover new trypanothione synthetase inhibitors
topic_facet	Ciencias Exactas Medicina Ensemble learning Machine learning QSAR Trypanosoma cruzi Chagas disease Trypanothione synthetase
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.
format	Articulo Articulo
author	Alice, Juan Ignacio Bellera, Carolina Leticia Benítez Boné, Diego Raúl Comini, Marcelo A. Duchowicz, Pablo Román Talevi, Alan
author_facet	Alice, Juan Ignacio Bellera, Carolina Leticia Benítez Boné, Diego Raúl Comini, Marcelo A. Duchowicz, Pablo Román Talevi, Alan
author_sort	Alice, Juan Ignacio
title	Ensemble learning application to discover new trypanothione synthetase inhibitors
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
publishDate	2021
url	http://sedici.unlp.edu.ar/handle/10915/133709
work_keys_str_mv	AT alicejuanignacio ensemblelearningapplicationtodiscovernewtrypanothionesynthetaseinhibitors AT belleracarolinaleticia ensemblelearningapplicationtodiscovernewtrypanothionesynthetaseinhibitors AT benitezbonediegoraul ensemblelearningapplicationtodiscovernewtrypanothionesynthetaseinhibitors AT cominimarceloa ensemblelearningapplicationtodiscovernewtrypanothionesynthetaseinhibitors AT duchowiczpabloroman ensemblelearningapplicationtodiscovernewtrypanothionesynthetaseinhibitors AT talevialan ensemblelearningapplicationtodiscovernewtrypanothionesynthetaseinhibitors
bdutipo_str	Repositorios
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Ensemble learning application to discover new trypanothione synthetase inhibitors

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