PyTorch Geometric (2.0.2) and PyTorch (1.10.1+cu102) functionalities were used for GNN training. Training was performed on a GPU (NVIDIA A100 Tensor Core-GPU) graphical processing unit for four hours, using a batch size of 16 samples. The Adam stochastic gradient descent optimizer was employed , with a learning rate of 10 -4 , mean squared error (MSE) loss on the training set, a decay factor of 0.5 applied after 100 epochs, and an exponential smoothing factor of 0.9. Early stopping was applied to the model that achieved the lowest validation mean absolute error (MAE) in 1000 epochs. All models considered in this study were trained on the Roche high-performance computing cluster, Switzerland.