The same train/test split as given by was used to compare the models that use different DES representations. The split consists in 55% training and 45% test. A Random forest (RF) regression model was trained with the default settings. Meanwhile, the hyperparameters of the GCN and the GNN-MG models were optimized with Optuna [4] using a 5-fold cross-validation. The final hyperparameter values are available in the Supplementary material. For benchmarking purposes, the number of epochs defined for training is the same as the baseline model , 1000 epochs. The meansquared error (MSE) was used as a loss function in all cases. The batch size was set to 32 for both GNN models. All models were run 10 times using the same split (but differing in the random initialization of the trainable weights) to obtain a measure of the performance intervals. The performance of all models was compared using the coefficient of determination R 2 , the mean absolute percentage error (MAPE), the mean absolute error (MAE), and the mean square error (MSE).