My main research orientation within bioinformatics is focused on the computational understanding and prediction of protein behavior, a critical pillar of modern molecular biology and drug discovery. The core of my current work lies in leveraging Protein Language Models (PLMs) and Deep Learning architectures, which allows us to treat protein sequences as a biological language to infer complex properties like three-dimensional structure, functional sites, and stability with unprecedented accuracy. Moving forward, my research is committed to an aggressive upgrade to advanced AI methodologies to tackle high-order biological complexity. Specifically, this involves applying Generative AI and Graph Neural Networks to problems such as mapping extensive protein-protein interaction (PPI) networks or the de novo design of novel therapeutic proteins not found in nature, thereby transitioning from predictive modeling to a powerful, data-driven approach to engineering biology.