Application of Data-Centric Supervised Machine Learning to Predict Phenotypic Activity Against Clinically Relevant Stages of <i>Trypanosoma cruzi</i>.

ABSTRACT: Background/Objectives: Chagas disease is a neglected tropical disease caused by the protozoan parasite Trypanosoma cruzi, which currently affects around 8 million people worldwide. The therapeutic arsenal against T. cruzi is so far limited to only two approved drugs, benznidazole and nifurtimox, that have considerable side effects and limited efficacy in the chronic stage of the disease. Here, we have resorted to supervised phenotypic machine learning models to explore drug repurposing opportunities and identify potential new therapeutic solutions for Chagas disease. Methods: More than 100,000 bioactivity data points were retrieved from ChEMBL and carefully curated according to the data-centric machine learning paradigm. After curation, two datasets comprising 3

SUBMITTER: Perez-Mauad N 

PROVIDER: S-EPMC12736270 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature