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

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 o...

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Bibliographic Details
Main Authors: Dingyan Wang, Jie Yu, Lifan Chen, Xutong Li, Hualiang Jiang, Kaixian Chen, Mingyue Zheng, Xiaomin Luo
Format: Article
Language:English
Published: BMC 2021-09-01
Series:Journal of Cheminformatics
Subjects:
Uncertainty quantification
Quantitative structure–activity relationship
Bayesian neural network
Applicability domain
Bayesian inference
Error prediction
Online Access:https://doi.org/10.1186/s13321-021-00551-x

Internet

https://doi.org/10.1186/s13321-021-00551-x

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