Uncertainty Quantification for Molecular Property Predictions with Graph Neural Architecture Search
arxiv(2023)
摘要
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. 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.
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