GAUCHE: A Library for Gaussian Processes in Chemistry

Ryan-Rhys Griffiths, Leo Klarner, Henry Moss, Aditya Ravuri, Sang T. Truong, Bojana Rankovic, Yuanqi Du, Arian Rokkum Jamasb, Julius Schwartz, Austin Tripp, Gregory Kell, Anthony Bourached, Alex Chan, Jacob Moss, Chengzhi Guo, Alpha Lee, Philippe Schwaller, Jian Tang

Keywords: Chemistry, Molecules, Gaussian processes, Bayesian optimization

TL;DR: A Gaussian process and Bayesian optimisation library for molecules

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

Track: Original Research Track

Community Implementations: [ 2 code implementations](https://www.catalyzex.com/paper/arxiv:2212.04450/code)