Multitask learning-driven identification of novel antitrypanosomal compounds.
Future Med Chem
; 15(16): 1449-1467, 2023 08.
Article
em En
| MEDLINE
| ID: mdl-37701989
ABSTRACT
Background:
Chagas disease and human African trypanosomiasis cause substantial death and morbidity, particularly in low- and middle-income countries, making the need for novel drugs urgent. Methodology &results:
Therefore, an explainable multitask pipeline to profile the activity of compounds against three trypanosomes (Trypanosoma brucei brucei, Trypanosoma brucei rhodesiense and Trypanosoma cruzi) were created. These models successfully discovered four new experimental hits (LC-3, LC-4, LC-6 and LC-15). Among them, LC-6 showed promising results, with IC50 values ranging 0.01-0.072 µM and selectivity indices >10,000.Conclusion:
These results demonstrate that the multitask protocol offers predictivity and interpretability in the virtual screening of new antitrypanosomal compounds and has the potential to improve hit rates in Chagas and human African trypanosomiasis projects.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Tripanossomicidas
/
Trypanosoma brucei brucei
/
Trypanosoma cruzi
/
Tripanossomíase Africana
/
Doença de Chagas
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
Limite:
Animals
/
Humans
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article