GAUCHE: A Library for Gaussian Processes in Chemistry
University of Cambridge · University of Oxford · Stanford University · Cornell University · Genentech · University of Toronto · EPFL · Roche / Genentech · Washington State University · N/A · King's College London, University of London · Department of Computer Science, University of Oxford · University College London · Evonik Industries AG · Google · Prescient Design, Genentech/Roche · Institute of Science and Technology (IST) Austria · Swiss Federal Institute of Technology Lausanne · Mila
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摘要
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.