Evidential Deep Learning for Guided Molecular Property Prediction and Discovery.

ABSTRACT: While neural networks achieve state-of-the-art performance for many molecular modeling and structure-property prediction tasks, these models can struggle with generalization to out-of-domain examples, exhibit poor sample efficiency, and produce uncalibrated predictions. In this paper, we leverage advances in evidential deep learning to demonstrate a new approach to uncertainty quantification for neural network-based molecular structure-property prediction at no additional computational cost. We develop both evidential 2D message passing neural networks and evidential 3D atomistic neural networks and apply these networks across a range of different tasks. We demonstrate that evidential uncertainties enable (1) calibrated predictions where uncertainty correlates with error, (2) sample-effici

SUBMITTER: Soleimany AP 

PROVIDER: S-EPMC8393200 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature