Machine learning (ML) techniques have enabled remarkable progress in the chemical and physical sciences, particularly in terms of fast and precise modelling of computationally-expensive processes. Graph neural networks (GNNs), a class of geometric deep learning algorithms, have recently emerged as one of the leading ML paradigms for learning directly on the data types occurring in the life sciences. Thanks to their ability to naturally learn from non-Euclidean data structures, represented as objects (nodes) and their connections (edges), GNNs have the potential to model complex relationships and dependencies between nodes. Successful examples include, but are not limited to tasks from particle physics , simulations of fluid dynamics and other physical systems , quantum chemistry , and drug discovery . However, such data-driven efforts are highly dependent on the quality, quantity, and availability of suitable data. Thus, a significant amount of research has been devoted to the development of high-quality datasets that support the rapidly advancing field of graph representation learning. In particular, challenging computational chemistry tasks such as quantum property prediction, drug-like molecule generation, and few-shot learning have put molecular data at the forefront of geometric deep learning research.