A benchmark study on reliable molecular supervised learning via Bayesian learning

2020/06/12 by Doyeong Hwang, Hwang, Doyeong, Grace Lee +7
Biochemistry, Genetics and Molecular Biology · Computer Science · Materials Science · #Computational Drug Discovery Methods #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning in Materials Science #Metabolomics and Mass Spectrometry Studies

paper · pdf · doi:10.48550/arxiv.2006.07021

openalex publication_date 2020/06/12 · openalex created_date 2020/06/19 · openalex updated_date 2026/07/28

Abstract

Virtual screening aims to find desirable compounds from chemical library by using computational methods. For this purpose with machine learning, model outputs that can be interpreted as predictive probability will be beneficial, in that a high prediction score corresponds to high probability of correctness. In this work, we present a study on the prediction performance and reliability of graph neural networks trained with the recently proposed Bayesian learning algorithms. Our work shows that Bayesian learning algorithms allow well-calibrated predictions for various GNN architectures and classification tasks. Also, we show the implications of reliable predictions on virtual screening, where Bayesian learning may lead to higher success in finding hit compounds.

Citations

• Semi-Supervised Classification with Graph Convolutional Networks
• Inductive Representation Learning on Large Graphs
• Residual Gated Graph ConvNets
• Averaging Weights Leads to Wider Optima and Better Generalization
• How Powerful are Graph Neural Networks?
• A Simple Baseline for Bayesian Uncertainty in Deep Learning
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• Deep Ensembles: A Loss Landscape Perspective
• Bayesian Deep Learning and a Probabilistic Perspective of Generalization

Related

• A comprehensive study on the prediction reliability of graph neural networks for virtual screening
• Bayesian Graph Neural Networks for Molecular Property Prediction
• Large-scale Pretraining Improves Sample Efficiency of Active Learning based Molecule Virtual Screening
• Leveraging Classical Algorithms for Graph Neural Networks
• Uncertainty quantification of molecular property prediction using Bayesian neural network models