Table contains the metrics for the deep-learning model, depicting accuracy, area under the ROC curve (AUC) and binary cross-entropy loss. Accuracy measures the number of correct predictions as a percentage of the total predictions. AUC is a slightly different but nonetheless important metric that demonstrates the model's ability to discriminate between two cases, which in this study are being an inhibitor or being a non-inhibitor. Our model's average AUC of 0.969 can be interpreted as meaning that 96.9% of the time, the model will correctly output a higher probability of inhibition for a randomly selected inhibitor than a randomly selected non-inhibitor, even if the overall classification is incorrect.