Graph Neural Network Powered Bayesian Optimization for Large Molecular Spaces
Abstract
*In silico* screening is an essential component of drug and materials discovery. This is challenged by the increasingly intractable size of virtual libraries and the high cost of evaluating properties. We propose GNN-SS, a Graph Neural Network-powered Bayesian Optimization (BO) algorithm as a scalable solution. GNN-SS utilizes random sub-sampling to reduce the computational complexity of the BO problem, and diversifies queries for training the model. GNN-SS is sample-efficient, and rapidly narrows the search space by leveraging the generalization ability of GNNs. Our algorithm performs competitively on the QM9 dataset and achieves state-of-the-art performance amongst screening algorithms on the PMO benchmark.
Cite
Text
Wang-Henderson et al. "Graph Neural Network Powered Bayesian Optimization for Large Molecular Spaces." ICML 2023 Workshops: SPIGM, 2023.Markdown
[Wang-Henderson et al. "Graph Neural Network Powered Bayesian Optimization for Large Molecular Spaces." ICML 2023 Workshops: SPIGM, 2023.](https://mlanthology.org/icmlw/2023/wanghenderson2023icmlw-graph/)BibTeX
@inproceedings{wanghenderson2023icmlw-graph,
title = {{Graph Neural Network Powered Bayesian Optimization for Large Molecular Spaces}},
author = {Wang-Henderson, Miles and Soyuer, Bartu and Kassraie, Parnian and Krause, Andreas and Bogunovic, Ilija},
booktitle = {ICML 2023 Workshops: SPIGM},
year = {2023},
url = {https://mlanthology.org/icmlw/2023/wanghenderson2023icmlw-graph/}
}