aweSOM: a GNN-based Site-of-Metabolism Predictor with Aleatoric and Epistemic Uncertainty Estimation

Accurate determination of the metabolic fate of xenobiotics is essential for ensuring their safety and efficacy. While in vivo and in vitro methods remain the gold standard for assessing metabolic properties, they are both costly and time-consuming. In silico metabolism prediction models offer complementary solutions to tackle the challenging task of improving a compound's metabolic stability without compromising its desired biological activity. This paper introduces aweSOM, a novel graph neural network (GNN)-based site-of-metabolism (SOM) predictor for phase 1 and 2 metabolic reactions. aweSOM leverages deep ensemble learning to decompose the total predictive uncertainty into its aleatoric and epistemic components. Through a detailed analysis of experimental results and case studies, we show how these uncertainty estimates enhance prediction reliability, provide valuable insights to guide future model improvements and data expansion strategies, and help to improve the efficiency of experimental metabolic characterization for novel compounds.