Hyperparameter optimization was performed using a grid search, considering RMSD cutoff value to define an edge (RMSD cut-off), number of adjacent nodes to randomly sample for aggregation (nrof-neigh), and graph loss weight (lossg weight) as hyperparameters, for a total of 30 combinations (Table ). Training and validation sets were prepared by using the split_train_val_dbx2 routine of the DBX2 package. The graphs generated from PDBbind 2016 complexes were split as follows: the graph was created with the number of nodes per graph of 140. Then, the data was split for stratified 5-fold cross-validation (90% training, 10% validation), with each fold maintaining a consistent distribution of protein families (e.g. T4 lysozyme, β-galactosidase, etc.) across all folds. Node and edge features for each graph were normalized using standard scaler.