Geometric deep learning (GDL) approaches are increasingly used across the life sciences, with remarkable potential and achievements in computational biology (analysing single-cell sequencing data ), structural biology (prediction of protein structures and protein sequence design ), drug discovery and simulating rigid and fluid dynamics being only a few examples. The simple but powerful formulation of GDL methods such as graph neural networks (GNN) motivated the investigation of long-standing problems from a new perspective, particularly in fields such as computational chemistry where the GDL abstractions can be naturally applied to objects like atoms and molecules (nodes and graphs), as well as their interactions (edges).