While iterative updates improve the model's ability to capture complex atomic environments, they also increase computational cost as molecular size grows, Nonetheless, this approach remains relatively inexpensive and computationally efficient compared to more complex architectures. Furthermore, like traditional GNNs, MPGNNs typically restrict message-passing to a fixed set of neighbouring nodes, potentially missing critical long-range interactions. However, some advanced message-passing strategies allow information to flow between atoms separated by multiple bonds, helping to mitigate this limitation. MPGNNs have been widely used in chemistry, especially for molecular property prediction. For more information on MPGNNs, please refer to Ref. .