A Bayesian graph convolutional network for reliable prediction of molecular properties with uncertainty quantification.

ABSTRACT: Deep neural networks have been increasingly used in various chemical fields. In the nature of a data-driven approach, their performance strongly depends on data used in training. Therefore, models developed in data-deficient situations can cause highly uncertain predictions, leading to vulnerable decision making. Here, we show that Bayesian inference enables more reliable prediction with quantitative uncertainty analysis. Decomposition of the predictive uncertainty into model- and data-driven uncertainties allows us to elucidate the source of errors for further improvements. For molecular applications, we devised a Bayesian graph convolutional network (GCN) and evaluated its performance for molecular property predictions. Our study on the classification problem of bio-activity and toxicity

SUBMITTER: Ryu S 

PROVIDER: S-EPMC6839511 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature