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Ensemble learning application to discover new trypanothione synthetase inhibitors

Abstract

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.

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Acknowledgements

Juan I. Alice, Carolina L. Bellera, Pablo R. Duchowicz and Alan Talevi thank the National University of La Plata (UNLP) and the Argentinean National Council of Scientific and Technical Research Council (CONICET). The present work was funded by The National Agency of Scientific and Technological Promotion (ANPCyT PICT 2017-0643 and PICT 2016-02056), UNLP (National University of La Plata, 11/X785), and the CONICET PIP11220130100311 project. Diego Benítez and Marcelo A. Comini belong to the Uruguayan National System of Researchers (SNI) from ANII and thank the support of FOCEM (Fondo para la Convergencia Estructural del Mercosur, COF 03/11).

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Correspondence to Alan Talevi.

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Alice, J.I., Bellera, C.L., Benítez, D. et al. Ensemble learning application to discover new trypanothione synthetase inhibitors. Mol Divers 25, 1361–1373 (2021). https://doi.org/10.1007/s11030-021-10265-9

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Keywords

  • Ensemble learning
  • Machine learning
  • QSAR
  • Trypanosoma cruzi
  • Chagas disease
  • Trypanothione synthetase