Accepted author manuscript, 748 KB, PDF document
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
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TY - GEN
T1 - GAUCHE
T2 - a library for Gaussian processes in chemistry
AU - Griffiths, Ryan-Rhys
AU - Klarner, Leo
AU - Moss, Henry
AU - Ravuri, Aditya
AU - Truong, Sang
AU - Du, Yuanqi
AU - Stanton, Samuel
AU - Tom, Gary
AU - Rankovic, Bojana
AU - Jamasb, Arian
PY - 2024/12/9
Y1 - 2024/12/9
N2 - We introduce GAUCHE, an open-source library for GAUssian processes inCHEmistry. Gaussian processes have long been a cornerstone of probabilisticmachine 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.
AB - We introduce GAUCHE, an open-source library for GAUssian processes inCHEmistry. Gaussian processes have long been a cornerstone of probabilisticmachine 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.
M3 - Conference contribution/Paper
T3 - Advances in Neural Information Processing Systems
BT - NeurIPS Proceedings 2024
ER -