All EGAT models were trained using the Adam optimizer and a batch size of 50. The learning rate, η, was initially set to 5e-4, linearly increased each update step for 10 epochs to 1e-3, followed by an exponential decay to a minimum of 1e-5. Early stopping was applied to terminate training if the validation loss did not decrease in 30 consecutive epochs. Further information on the hyperparameters for each model can be found in the Supplemental Information. All Chemprop models were trained using the optimized parameters found via the hyperopt package listed in the Supplemental Information (SI) section of the Heid et. al. paper.