Although existing SOM-predictors report adequate performance on holdout data, there is a significant need for more accurate assessments of model applicability and predictive uncertainty. To address these needs, we propose AWESOM, a GNN-based SOM-predictor trained on the METAQSAR database , a high-quality proprietary data resource covering a wide range of phase 1 and 2 metabolic reactions of drug-like, xenobiotic substrates. Inspired by the works of Scalia , Heid and Yang and Li , our framework employs deep ensembles , an approximation method to Bayesian neural networks (BNNs), to provide a separate estimation of the epistemic and aleatoric contributions to the total predictive uncertainty. This separation has several benefits. First, it allows end-users to better assess the reliability of the model's predictions. Second, it offers insights to future model developers into the predictor's principal limitations and potential sources of error. Third, it highlights problematic compounds or structural motifs that require additional data, guiding meaningful data expansion.