Multitask learning-driven identification of novel antitrypanosomal compounds

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.

Medienart:

E-Artikel

Erscheinungsjahr:

2023

Erschienen:

2023

Enthalten in:

Zur Gesamtaufnahme - volume:15

Enthalten in:

Future medicinal chemistry - 15(2023), 16 vom: 13. Aug., Seite 1449-1467

Sprache:

Englisch

Beteiligte Personen:

Lemos, Jade Milhomem [VerfasserIn]
Brito da Silva, Meryck Felipe [VerfasserIn]
Dos Santos Carvalho, Alexandra Maria [VerfasserIn]
Vicente Gil, Henric Pietro [VerfasserIn]
Fiaia Costa, Vinícius Alexandre [VerfasserIn]
Andrade, Carolina Horta [VerfasserIn]
Braga, Rodolpho Campos [VerfasserIn]
Grellier, Philippe [VerfasserIn]
Muratov, Eugene N [VerfasserIn]
Charneau, Sébastien [VerfasserIn]
Moreira-Filho, José Teófilo [VerfasserIn]
Dourado Bastos, Izabela Marques [VerfasserIn]
Neves, Bruno Junior [VerfasserIn]

Links:

Volltext

Themen:

Deep learning
Journal Article
Low-data regimes
Model explainability
Neglected tropical diseases
QSAR
Research Support, Non-U.S. Gov't
Trypanocidal Agents
Trypanosomatids
Virtual screening

Anmerkungen:

Date Completed 11.10.2023

Date Revised 12.10.2023

published: Print-Electronic

Citation Status MEDLINE

doi:

10.4155/fmc-2023-0074

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM361993196