A hybrid framework for improving uncertainty quantification in deep learning-based QSAR regression modeling.

ABSTRACT: Reliable uncertainty quantification for statistical models is crucial in various downstream applications, especially for drug design and discovery where mistakes may incur a large amount of cost. This topic has therefore absorbed much attention and a plethora of methods have been proposed over the past years. The approaches that have been reported so far can be mainly categorized into two classes: distance-based approaches and Bayesian approaches. Although these methods have been widely used in many scenarios and shown promising performance with their distinct superiorities, being overconfident on out-of-distribution examples still poses challenges for the deployment of these techniques in real-world applications. In this study we investigated a number of consensus strategies in order to c

SUBMITTER: Wang D 

PROVIDER: S-EPMC8454160 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature