The d-MPNN model takes the molecular graph as input, and performs several steps of message passing to update atom and bond features with information from their neighborhood to yield an atomic representation. A molecular representation is then obtained by aggregating the atomic representations using an aggregation function such as summing or averaging. Subsequently, a feedforward neural network transforms the learned molecular representation into the respective target property. Unless otherwise noted, d-MPNN models trained on the synthetic group additivity dataset use a hidden size of 1000, four steps of message passing, two feed-forward layers, scaled sum aggregation (called "norm" in Chemprop), and 200 epochs of training unless otherwise indicated. In contrast, d-MPNN models trained on QM9 differ slightly by using a hidden size of 300. In the following, if the hidden size is specified that is the hidden size in both the d-MPNN and the FFNN part. All other hyperparameters were chosen according to their default values in Chemprop.