GAUCHE: A Library for Gaussian Processes in Chemistry
This provides a tool for chemists to leverage probabilistic machine learning in experimental scenarios, though it is incremental as it adapts existing methods to a specific domain.
The authors tackled the challenge of applying Gaussian processes to chemistry by introducing GAUCHE, a library that defines kernels for structured chemical inputs like graphs and strings, enabling uncertainty quantification and Bayesian optimization for molecular discovery and reaction optimization.
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