Message-passing graph neural networks (MPGNNs) are an extension of GNNs that include a messagepassing mechanism, where nodes iteratively exchange information with their neighbours to update their representation based on their own environment and the environments of surrounding nodes. After multiple rounds of message-passing, the representation of the atom is no longer defined solely by its own attributes but is updated to reflect its interactions with neighbouring atoms. The message-passing process is entirely learnable, making MPGNNs well-suited to representing chemical structures as the relationships between atoms are critical for understanding the behaviour of molecular systems.