Uncertainty quantification of molecular property prediction using Bayesian neural network models

2018/11/19 by Seongok Ryu, Ryu, Seongok, Yongchan Kwon +3
Computer Science · Engineering · Materials Science · #Chemical Physics (physics.chem-ph) #Computational Drug Discovery Methods #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning in Materials Science #Process Optimization and Integration

paper · pdf · doi:10.48550/arxiv.1905.06945

openalex publication_date 2018/11/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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 increase in the use of state-of-the-art models and algorithms, deep neural network models often produce poor predictions in real applications because model performance is highly dependent on the quality of training data. In the field of molecular analysis, data are mostly obtained from either complicated chemical experiments or approximate mathematical equations, and then quality of data may be questioned.In this paper, we quantify uncertainties of prediction using Bayesian neural networks in molecular property predictions. We estimate both model-driven and data-driven uncertainties, demonstrating the usefulness of uncertainty quantification as both a quality checker and a confidence indicator with the three experiments. Our results manifest that uncertainty quantification is necessary for more reliable molecular applications and Bayesian neural network models can be a practical approach.

Citations

• Semi-Supervised Classification with Graph Convolutional Networks
• Quantum-chemical insights from deep tensor neural networks
• Multiplicative Normalizing Flows for Variational Bayesian Neural Networks
• What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
• Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models
• Learning Deep Generative Models of Graphs
• Junction Tree Variational Autoencoder for Molecular Graph Generation
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• Weight Uncertainty in Neural Networks
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