Explainable uncertainty quantifications for deep learning-based molecular property prediction

Explainable uncertainty quantifications for deep learning-based molecular property prediction

Abstract Quantifying uncertainty in machine learning is important in new research areas with scarce high-quality data. In this work, we develop an explainable uncertainty quantification method for deep learning-based molecular property prediction. This method can capture aleatoric and epistemic unce...

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Bibliographic Details
Main Authors: Chu-I Yang, Yi-Pei Li
Format: Article
Language:English
Published: BMC 2023-02-01
Series:Journal of Cheminformatics
Subjects:
Explainable AI
Uncertainty quantifications
Deep learning
Molecular property prediction
Online Access:https://doi.org/10.1186/s13321-023-00682-3

Internet

https://doi.org/10.1186/s13321-023-00682-3

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