Uncertainty Quantification for Molecular Property Predictions with Graph Neural Architecture Search
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.
Code (2)
Tasks
Decision MakingDrug DiscoveryMolecular Property PredictionNeural Architecture SearchPredictionProperty PredictionUncertainty QuantificationSimilar Papers 제목 키워드 기반
Uncertainty quantification of molecular property prediction using Bayesian neural network models
In chemistry, deep neural network models have been increasingly utilized in a variety of applications such as molecular property predictions, novel molecule designs, and planning chemical reactions. Despite the rapid inc…
Molecular Property PredictionProperty PredictionUncertainty QuantificationUncertainty quantification of molecular property prediction with Bayesian neural networks
Deep neural networks have outperformed existing machine learning models in various molecular applications. In practical applications, it is still difficult to make confident decisions because of the uncertainty in predic…
Molecular Property PredictionProperty PredictionUncertainty QuantificationEvaluating Scalable Uncertainty Estimation Methods for DNN-Based Molecular Property Prediction
Advances in deep neural network (DNN) based molecular property prediction have recently led to the development of models of remarkable accuracy and generalization ability, with graph convolution neural networks (GCNNs) r…
Bayesian InferenceMolecular Property PredictionProperty PredictionUncertainty QuantificationUncertainty Quantification Using Neural Networks for Molecular Property Prediction
Uncertainty quantification (UQ) is an important component of molecular property prediction, particularly for drug discovery applications where model predictions direct experimental design and where unanticipated imprecis…
Drug DiscoveryExperimental DesignMolecular Property PredictionProperty Prediction+1Improved Uncertainty Estimation of Graph Neural Network Potentials Using Engineered Latent Space Distances
Graph neural networks (GNNs) have been shown to be astonishingly capable models for molecular property prediction, particularly as surrogates for expensive density functional theory calculations of relaxed energy for nov…
Graph Neural NetworkMolecular Property PredictionPredictionProperty Prediction+1