Issue 8, 2024

Uncertainty quantification for molecular property predictions with graph neural architecture search

Abstract

Graph Neural Networks (GNNs) have emerged as a prominent class of data-driven methods for molecular property prediction. However, a key limitation of typical GNN models is their inability to quantify uncertainties in the predictions. This capability is crucial for ensuring the trustworthy use and deployment of models in downstream tasks. To that end, we introduce AutoGNNUQ, an automated uncertainty quantification (UQ) approach for molecular property prediction. AutoGNNUQ leverages architecture search to generate an ensemble of high-performing GNNs, enabling the estimation of predictive uncertainties. Our approach employs variance decomposition to separate data (aleatoric) and model (epistemic) uncertainties, providing valuable insights for reducing them. In our computational experiments, we demonstrate that AutoGNNUQ outperforms existing UQ methods in terms of both prediction accuracy and UQ performance on multiple benchmark datasets, and generalizes well to out-of-distribution datasets. Additionally, we utilize t-SNE visualization to explore correlations between molecular features and uncertainty, offering insight for dataset improvement. AutoGNNUQ has broad applicability in domains such as drug discovery and materials science, where accurate uncertainty quantification is crucial for decision-making.

Graphical abstract: Uncertainty quantification for molecular property predictions with graph neural architecture search

Supplementary files

Article information

Article type
Paper
Submitted
01 Apr 2024
Accepted
07 Jun 2024
First published
25 Jun 2024
This article is Open Access
Creative Commons BY-NC license

Digital Discovery, 2024,3, 1534-1553

Uncertainty quantification for molecular property predictions with graph neural architecture search

S. Jiang, S. Qin, R. C. Van Lehn, P. Balaprakash and V. M. Zavala, Digital Discovery, 2024, 3, 1534 DOI: 10.1039/D4DD00088A

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