Kendall et al. demonstrated how to model both epistemic and aleatoric uncertainties in deep learning using MC-dropout and maximum a posteriori (MAP) estimation inference, respectively. They specifically advocated for a distinction between aleatoric and epistemic uncertainty in the context of deep-learning in order to improve the limited awareness of neural networks of their own competence. Zhang et al. and Ryu et al. independently adapted this strategy to predict continuousvalued molecular properties with uncertainty quantification using graph neural networks. Scalia et al. further refined the approach, demonstrating that the ensemble-based approach proposed by Lakshminarayanan et al. outperformed MC-dropout for deep learning-based molecular property regression.