GAUCHE: A Library for Gaussian Processes in Chemistry
Ryan-Rhys Griffiths, Leo Klarner, Henry Moss, Aditya Ravuri, Sang T. Truong, Yuanqi Du, Samuel Don Stanton, Gary Tom
Abstract
We introduce GAUCHE, an open-source 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 molecular representations, however, necessitates kernels defined over structured inputs such as graphs, strings and bit vectors. By providing such kernels in a modular, robust and easy-to-use framework, we seek to enable expert chemists and materials scientists to make use of state-of-the-art black-box optimization techniques. Motivated by scenarios frequently encountered in practice, we showcase applications for GAUCHE in molecular discovery, chemical reaction optimisation and protein design. The codebase is made available at https://github.com/leojklarner/gauche.
BibTeX
@inproceedings{
griffiths2023gauche,
title={{GAUCHE}: A Library for Gaussian Processes in Chemistry},
author={Ryan-Rhys Griffiths and Leo Klarner and Henry Moss and Aditya Ravuri and Sang T. Truong and Yuanqi Du and Samuel Don Stanton and Gary Tom and Bojana Rankovi{\'c} and Arian Rokkum Jamasb and Aryan Deshwal and Julius Schwartz and Austin Tripp and Gregory Kell and Simon Frieder and Anthony Bourached and Alex James Chan and Jacob Moss and Chengzhi Guo and Johannes P. D{\"u}rholt and Saudamini Chaurasia and Ji Won Park and Felix Strieth-Kalthoff and Alpha Lee and Bingqing Cheng and Alan Aspuru-Guzik and Philippe Schwaller and Jian Tang},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
year={2023},
url={https://openreview.net/forum?id=vzrA6uqOis}
}