GAUCHE: A Library for Gaussian Processes in Chemistry

TL;DR

GAUCHE is a specialized library that extends Gaussian processes to structured chemical data, enabling advanced uncertainty quantification and Bayesian optimization for molecular discovery and reaction optimization.

Contribution

It introduces kernels for structured chemical representations within Gaussian processes, facilitating their application in chemistry-related machine learning tasks.

Findings

Effective kernels for graphs, strings, and bit vectors in chemical data

Successful application to molecular discovery tasks

Enhanced Bayesian optimization in chemical research

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