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
@article{arxiv.2212.04450,
title = {GAUCHE: A Library for Gaussian Processes in Chemistry},
author = {Ryan-Rhys Griffiths and Leo Klarner and Henry B. Moss and Aditya Ravuri and Sang Truong and Samuel Stanton and Gary Tom and Bojana Rankovic and Yuanqi Du and Arian Jamasb and Aryan Deshwal and Julius Schwartz and Austin Tripp and Gregory Kell and Simon Frieder and Anthony Bourached and Alex Chan and Jacob Moss and Chengzhi Guo and Johannes Durholt and Saudamini Chaurasia and Felix Strieth-Kalthoff and Alpha A. Lee and Bingqing Cheng and Alán Aspuru-Guzik and Philippe Schwaller and Jian Tang},
journal= {arXiv preprint arXiv:2212.04450},
year = {2023}
}