Protein-based drugs are becoming some of the most important drugs of the XXI century. The typical mechanism of action of these drugs is a strong protein-protein interaction (PPI) between surfaces with complementary geometry and chemistry. Over the past three decades, large amounts of structural data on PPIs has been collected, creating opportunities for differentiable learning on the surface geometry and chemical properties of natural PPIs. Since the surface of these proteins has a non-Euclidean structure, it is a natural fit for geometric deep learning, a novel class of machine learning techniques generalising successful neural architectures to manifolds and graphs. In the talk, I will show how geometric deep learning methods can be used to address various problems in functional protein design such as interface site prediction, pocket classification, and search for surface motifs. These methods can potentially open new possibilities in designing novel drugs for "undruggable" targets.