This document summarizes an approach using cheminformatics and bioinformatics to analyze big data related to neglected tropical diseases, specifically applying it to Chagas disease. Key aspects included curating the Trypanosoma cruzi metabolome, developing machine learning models to predict active compounds from screening data, screening over 7,500 compounds and identifying hits, and validating the top 5 hits in vitro and in vivo in a mouse model. One particularly promising hit was pyronaridine, which showed strong anti-trypanosomal activity and is an approved antimalarial, highlighting its potential for repurposing for Chagas disease.