After the deep-learning algorithm was made and fine-tuned, it was run 5 times on a dataset of 2151 FDA-approved molecules with the same 160 descriptors as the training set. Figure shows a graph of the training metrics of the model while training for each of the 5 trials. The fact that the accuracy and AUC start at 0.5, or random chance, but increase consistently with the epoch number, shows that the model is effectively learning and improving over time. The 5 trials applied had an average validation accuracy of 91.6% and an area under the receiveroperator characteristic (ROC) curve (AUC) of 0.969 (Table ).