A detailed description of the EGNN model including a combination of molecular GNN combined with docking is given in the Experimental Section. Figure shows that the EGNN model is a combination of local features of the small molecule represented as a GNN (see Graph Neural Network for Molecular Graphs in EGNN) along with global features of protein-ligand interaction represented as docking scores (see section Generation of Energy Features with Docking and Energy Vector (E) in EGNN). The EGNN was trained with 88 small molecules with high and low potency for PD-1/PD-L1 inhibition extracted from two Incyte patents (see Patent Data for Training the EGNN Model). We calculated variation in the average F1 score (over five crossvalidated folds) versus the number of epochs for different hyperparameters (Figure ). Also, model's performance with different datasets with changing hyperparameters are shown in Table . Optimal hyperparameters were selected to avoid overfitting and underfitting for EGNN.