Multitask learning-driven identification of novel antitrypanosomal compounds.

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. Therefore, an explainable multitask pipeline to profile the activity of compounds against three trypanosomes ( and ) were created. These models successfully discovered four new experimental hits (, , and ). Among them, showed promising results, with IC values ranging 0.01-0.072 μM and selectivity indices >10,000. 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.