GAUCHE: A Library for Gaussian Processes in Chemistry
Abstract
We introduce GAUCHE, a library for GAUssian processes in CHEmistry. Gaussian processes have long been a cornerstone of probabilistic machine learning, affording particular advantages for uncertainty quantification and Bayesian optimisation. Extending Gaussian processes to chemical representations however is nontrivial, necessitating kernels defined over structured inputs such as graphs, strings and bit vectors. By defining such kernels in GAUCHE, we seek to open the door to powerful tools for uncertainty quantification and Bayesian optimisation in chemistry. Motivated by scenarios frequently encountered in experimental chemistry, we showcase applications for GAUCHE in molecular discovery and chemical reaction optimisation. The codebase is made available at https://github.com/leojklarner/gauche
Cite
Text
Griffiths et al. "GAUCHE: A Library for Gaussian Processes in Chemistry." ICML 2022 Workshops: AI4Science, 2022.Markdown
[Griffiths et al. "GAUCHE: A Library for Gaussian Processes in Chemistry." ICML 2022 Workshops: AI4Science, 2022.](https://mlanthology.org/icmlw/2022/griffiths2022icmlw-gauche/)BibTeX
@inproceedings{griffiths2022icmlw-gauche,
title = {{GAUCHE: A Library for Gaussian Processes in Chemistry}},
author = {Griffiths, Ryan-Rhys and Klarner, Leo and Moss, Henry and Ravuri, Aditya and Truong, Sang T. and Rankovic, Bojana and Du, Yuanqi and Jamasb, Arian Rokkum and Schwartz, Julius and Tripp, Austin and Kell, Gregory and Bourached, Anthony and Chan, Alex and Moss, Jacob and Guo, Chengzhi and Lee, Alpha and Schwaller, Philippe and Tang, Jian},
booktitle = {ICML 2022 Workshops: AI4Science},
year = {2022},
url = {https://mlanthology.org/icmlw/2022/griffiths2022icmlw-gauche/}
}