GAUCHE: A Library for Gaussian Processes in ChemistryDownload PDF

21 May 2022, 02:42 (modified: 08 Jul 2022, 01:50)ICML-AI4Science PosterReaders: Everyone
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
Track: Original Research Track
0 Replies