Uncertainty Quantification for Molecular Property Predictions with Graph Neural Architecture Search

Understand

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.