Building on Scalia's and Heid's works, Yang et al. developed an uncertainty quantification framework for deep learning-based molecular property prediction, which infers the contributions of individual atoms to the overall molecular aleatoric and epistemic uncertainties. They also proposed a post-hoc calibration method to refine the uncertainty quantified by ensemble models for better confidence interval estimates.