To create our price prediction GNN we represented compounds as 2D graphs with atoms corresponding to nodes and bonds to edges. Nodes are encoded using five features: atomic number, valence, formal charge, number of neighbours, and aromaticity. Edges are represented with four features: bond type, aromaticity, conjugation, and ring membership. Our GNN first embeds the node and edge features using a learnable linear transformation from dimension five and four to 75 and 50 respectively. After that, ten blocks consisting of a PNA layer , batch normalization and ReLU 52 activation are applied one after another. Then, an embedding for the graph is obtained applying a Set2set layer . Finally, two dense layers with batch normalization and ReLU activation and one last linear layer with one single unit are applied to the graph embedding. A schematic of our GNN architecture is depicted in Figure . The hyperparameters were selected by cross-validation over the validation dataset, exhibiting a robust behaviour. See Supplementary Information Section 9 for more details. Our network was trained using the Adam optimizer with a batch size of 512 graphs. Initial learning rate was set to 10 -5 and decreased by a factor of 0.1 when the validation loss did not improve during 25 epochs. The mean squared error was used as the loss function.