A benchmark study on reliable molecular supervised learning via Bayesian learning

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.

arXiv commentTo be appeared in ICML 2020 Workshop "Uncertainty and Robustness in Deep Learning"

References in corpus (12)

• Semi-Supervised Classification with Graph Convolutional Networks
• Inductive Representation Learning on Large Graphs
• Weight Uncertainty in Neural Networks
• Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift
• How Powerful are Graph Neural Networks?
• Deep Ensembles: A Loss Landscape Perspective
• Residual Gated Graph ConvNets
• Benchmarking Graph Neural Networks
• Averaging Weights Leads to Wider Optima and Better Generalization
• Bayesian Deep Learning and a Probabilistic Perspective of Generalization
• A Simple Baseline for Bayesian Uncertainty in Deep Learning
• Preconditioned Stochastic Gradient Langevin Dynamics for Deep Neural Networks

Cited by in corpus (4)

• Gaussian Process Molecule Property Prediction with FlowMO
• Reliable Graph Neural Networks for Drug Discovery Under Distributional Shift
• Bayesian Graph Neural Networks for Molecular Property Prediction
• Ranking over Regression for Bayesian Optimization and Molecule Selection