Rodolpho C. Braga

This paper introduces a contrastive graph neural network to discover novel larvicidal compounds against Aedes aegypti, identifying LC-79 as a potent and selective candidate.

The paper introduces a semantic-aware graph neural network framework for drug discovery, identifying LC-61 as a potent antileishmanial compound with nanomolar activity.

The paper presents a framework that predicts endocrine disruptors using a multimodal cross-attentive graph model, achieving high accuracy and providing mechanistic explanations.

The paper uses machine learning to identify novel HDAC6/AKT2 inhibitors for myeloid cancer treatment, finding compounds with potent anticancer activity.