  In chemistry, deep neural network models have been increasingly utilized in a
variety of applications such as molecular property predictions, novel molecule
designs, and planning chemical reactions. Despite the rapid increase in the use
of state-of-the-art models and algorithms, deep neural network models often
produce poor predictions in real applications because model performance is
highly dependent on the quality of training data. In the field of molecular
analysis, data are mostly obtained from either complicated chemical experiments
or approximate mathematical equations, and then quality of data may be
questioned.In this paper, we quantify uncertainties of prediction using
Bayesian neural networks in molecular property predictions. We estimate both
model-driven and data-driven uncertainties, demonstrating the usefulness of
uncertainty quantification as both a quality checker and a confidence indicator
with the three experiments. Our results manifest that uncertainty
quantification is necessary for more reliable molecular applications and
Bayesian neural network models can be a practical approach.

Chemical Physics;Machine Learning;Machine Learning