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

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 p...

Descripción completa

Detalles Bibliográficos
Autores principales: Wang, Dingyan, Yu, Jie, Chen, Lifan, Li, Xutong, Jiang, Hualiang, Chen, Kaixian, Zheng, Mingyue, Luo, Xiaomin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2021
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8454160/
https://www.ncbi.nlm.nih.gov/pubmed/34544485
http://dx.doi.org/10.1186/s13321-021-00551-x

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8454160/
https://www.ncbi.nlm.nih.gov/pubmed/34544485
http://dx.doi.org/10.1186/s13321-021-00551-x

Ejemplares similares