Desenvolvimento de uma Ferramenta Baseada em Inteligência Artificial para a Predição de Desregulação Endócrina

Currently, studies on the reproductive toxicity of chemicals can be conducted through both in vitro and in vivo approaches. However, the implementation of these experimental assays faces numerous challenges, including the limited capacity for data processing given the vast number of commercial chemicals, the operational complexity of the tests, and ethical concerns related to the use of animal models. This work aims to develop artificial intelligence models that serve as alternative methods to predict the toxicity and adverse effect pathways of endocrine disruptors potentially harmful to both the male and female reproductive systems. Initially, datasets of compounds tested in vitro were compiled from the Tox21 and ToxCast databases, from which single-task models (Random Forest, SVM, LightGBM) and multi-task models (MT-DNN) were developed using ECFP4 fingerprints as molecular descriptors.