Details of Research Outputs

As uncertainty quantification is crucial for determining undesirable inputs and improving decisions made by a system to acquire accurate evaluations, it has received much attention in recent years. Motivated by the fact that probability is one of the most effective ways to estimate uncertainty, in this work we propose an effective probabilistic model for quantifying predictive uncertainty in the task of predicting chemical molecular properties. Our model is formulated by developing a spherical mixture density network that is composed of von Mises-Fisher kernel distributions to model graph-structured molecule representations. Furthermore, an ensemble framework for spherical mixture density networks is developed, which can yield high quality predictive uncertainty estimates and obtain better confidence intervals reflecting the sources of these uncertainties in predictions. The effectiveness of our approach in modeling the output predictive uncertainty is validated through empirical analysis on molecular property prediction tasks with two publicly available chemical molecule data sets. Comparing with the current state-of-the-art baselines, our model can better model predictive uncertainty in terms of higher log-likelihood of the data, and reveal that there might be more than one acceptable chemical property associated with an input molecule representation.