Based on these findings, we propose several recommendations for future model development. In cases of high aleatoric uncertainty, revisiting the training data to ensure accurate labeling is essential. This can be achieved by improving the standardization of SOM annotations across data sources and performing repeated experiments to reduce experimental variability. For high epistemic uncertainty, expanding the diversity of the training data, particularly by including rare substructures or uncommon biotransformations, is crucial. Given the costs of acquiring such data, prioritizing areas with high epistemic uncertainty ensures efficient use of resources and maximizes information gain.