Site-of-Metabolism Prediction with Aleatoric and Epistemic Uncertainty Quantification

In silico metabolism prediction models have become indispensable tools to optimize the metabolic properties of xenobiotics while preserving their intended biological activity. Among these, site-of-metabolism (SOM) prediction models are particularly valuable for pinpointing metabolically labile atomic positions. However, the practical utility of these models depends not only on their ability to deliver accurate predictions but also on their capacity to provide reliable estimates of predictive uncertainty. In this work, we introduce aweSOM, a graph neural network (GNN)-based SOM prediction model that leverages deep ensembling to model the total predictive accuracy and partition it into its aleatoric and epistemic components. We conduct a comprehensive evaluation of aweSOM's uncertainty estimates on a high-quality data set, identifying key challenges that currently constrain the performance of SOM prediction models. Based on these findings, we propose actionable insights to drive progress in the field of metabolism prediction.