The EGNN model trained with optimum hyperparameters and the Incyte dataset was used to get predictions for an in-house database of small molecular designs. We developed a bootstrapped EGNN model to predict compounds with high and low potency for PD-1/PD-L1 inhibition using 100 EGNN models (see section Bootstrapping the EGNN model). Bootstrapped EGNN models gave a F1 score of 0.91 and Kappa of 0.77 while bootstrapped GNN models gave 0.87 and 0.65 F1 score and Kappa respectively (Table ). Bootstrapping is an essential statistical technique that can be used to select confident molecules for synthesis and experimental validation based on agreements among multiple models. The bootstrapped EGNN model identified high and low potency small molecules as PD-1/PD-L1 inhibitors that were synthesized and then experimentally verified with HTRF binding assay (see Table for summary). Specifically, we selected 4 molecules predicted to be high or low potent for PD-1/PD-L1 inhibition for testing based on bootstrapped EGNN SoftMax average scores and standard deviation (see EGNN SoftMax scores in Table ).