Uncertainty quantification with graph neural networks for efficient molecular design

Uncertainty quantification with graph neural networks for efficient molecular design

Abstract Optimizing molecular design across expansive chemical spaces presents unique challenges, especially in maintaining predictive accuracy under domain shifts. This study integrates uncertainty quantification (UQ), directed message passing neural networks (D-MPNNs), and genetic algorithms (GAs)...

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Bibliographic Details
Main Authors:	Lung-Yi Chen, Yi-Pei Li
Format:	Article
Language:	English
Published:	Nature Portfolio 2025-04-01
Series:	Nature Communications
Online Access:	https://doi.org/10.1038/s41467-025-58503-0
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