Two datasets (BMS and Incyte) were used separately and in combination to train the EGNN model and determine the best dataset to predict PD-1/PD-L1 inhibitors. Splitting of the dataset into training-validation set and test set (4:1) were carried out using two different methods: (1) using a random splitter on shuffled data and (2) using a scaffold splitting method by DeepChem library. Then training was carried out with fivefold cross validation and test sets were used to evaluate the models' prediction ability. Here, Cohen's kappa, F1 score and Area Under the Receiver Operator Characteristic Curves (AUROC) were measured to compare three models trained with BMS data only, Incyte data only and BMS-Incyte combined data. Further, as a separate experiment, all the measures were obtained for EGNN and GNN models trained only on BMS data while predicting for Incyte data, and vice versa as well.