GNN models with scaffold splitting appeared to generate comparable results with the EGNN (Figure ). However, this was expected since the graph neural network uses the twodimensional molecular framework/topology in training. When the framework distributions of the compounds are similar in the train-validation and test sets, GNN performs well. However, our intension is to develop a model which could be used to screen a large compound library which would not be necessary to share the same distribution of scaffolds with training sets (i.e. Incyte or BMS). Hence, we selected the random splitter with shuffling to create the test set for performance evaluations to develop a more generalized model.