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.
Cite
@article{arxiv.2006.07021,
title = {A benchmark study on reliable molecular supervised learning via Bayesian learning},
author = {Doyeong Hwang and Grace Lee and Hanseok Jo and Seyoul Yoon and Seongok Ryu},
journal= {arXiv preprint arXiv:2006.07021},
year = {2020}
}
Comments
To be appeared in ICML 2020 Workshop "Uncertainty and Robustness in Deep Learning"