Cohen's kappa scores of different test sets (hold out test set is based on random splitting) for both models trained with BMS compounds, Incyte compounds, and the union of these sets are shown in Figure . The kappa scores of the EGNN and GNN models trained with Incyte data and tested on the hold out test set were 0.8861 and 0.4304, respectively (Figure ). This result suggests that the EGNN trained model with Incyte data that contains diverse chemical scaffolds (Figure ) performs much better than the GNN trained with the same data set. However, when the same test was done with only BMS compounds with lower chemical diversity than Incyte, the Cohen's kappa score is comparable for both models with 0.6416 for the EGNN model and 0.7164 for the GNN model. This suggests that the GNN model performs well with smaller chemical diversity in the training and test data as compared to larger chemical diversity. Both models show comparable performances with the combined datasets as well. When both BMS and Incyte datasets were combined, the kappa score for the hold out test set of the EGNN model was 0.6072 and 0.6729 for the GNN model. A similar trend is observed for the F1 scores for the three different training set comparisons (Figure ). These results suggest that the EGNN model outperforms the GNN model for chemically diverse data sets such as Incyte data. We believe this to be due to Incyte compounds, a Cohen's kappa score of 0.1505 for the EGNN and 0.1200 for the GNN was observed and F1 score of 0.3810 and 0.2264 were observed, respectively. On the other hand, both F1 and kappa scores for both models improved when they were trained with Incyte data and used to predict the BMS compounds (kappa score of EGNN = 0.3852, GNN = 0.3196 and F1 score of EGNN = 0.7400, GNN = 0.6958). These results show that there is a marked improvement in F1 scores and Cohen's kappa scores for both EGNN and GNN models when trained on Incyte data and tested on BMS. However, AUROC score cannot distinguish these models correctly (FigureS3). This was expected as the AUROC is a not good measure to evaluate models trained on skewed/unbalanced datasets and it can hinder the poor performance of a model. Also, these results suggest that the EGNN model outperforms the GNN model in both cases. (see Table for details). This highlights the importance of chemical diversity in training data even though there is not much compound similarity between the training and test sets. Therefore, it is not suitable to use only BMS or combined BMS and Incyte data to train the final model to make predictions for unknown molecules. Hence, we selected only the Incyte dataset to train the EGNN model which improved the EGNN model performance significantly (Table ). Extracted fingerprints from a molecular graph with the radius of 2 using Weisfeiler-Lehman algorithm were used as 'local' features similar to EGNN and GNN models. Here we have padded zeros up to the maximum fingerprint length to maintain the same fingerprint dimension. Same pre-selected docking scores (RCR15 and RCC15) obtained by CANDOCK were used as 'global' energy features. Obtained AUROC, AUPRC, Precision, Recall, F1 Score and Cohen's kappa values are tabulated in the Table for all four models. The SVM model was trained using the 'svm' package in scikit-learn library with the "linear" kernel and the RF was trained using the 'RandomForestClassifier' in scikit-learn library with 500 trees. The 'metrics' module in scikitlearn package was used for statistics AUROC, precision, recall, F1 score and Cohen's kappa.