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GAUCHE: A Library for Gaussian Processes in Chemistry
Ryan-Rhys Griffiths · Leo Klarner · Henry Moss · Aditya Ravuri · Sang Truong · Bojana Rankovic · Yuanqi Du · Arian Jamasb · Julius Schwartz · Austin Tripp · Gregory Kell · Anthony Bourached · Alex Chan · Jacob Moss · Chengzhi Guo · Alpha Lee · Philippe Schwaller · Jian Tang

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in Machine Learning and the Physical Sciences »

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.

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Author Information

Ryan-Rhys Griffiths (University of Cambridge)
Leo Klarner (University of Oxford)
Henry Moss (Secondmind)

I am a Senior Machine Learning Researcher at Secondmind (formerly PROWLER.io). I leverage information-theoretic arguments to provide efficient, reliable and scalable Bayesian optimisation for problems inspired by science and the automotive industry.

Aditya Ravuri (University of Cambridge)
Sang Truong (Stanford University)
Bojana Rankovic (EPFL)
Yuanqi Du (Cornell University)
Arian Jamasb (University of Cambridge)
Julius Schwartz (N/A)
Austin Tripp (University of Cambridge)
Gregory Kell (King's College London)
Anthony Bourached (University College London)
Alex Chan (University of Cambridge)
Jacob Moss (University of Cambridge)
Chengzhi Guo (University of Cambridge)
Alpha Lee (University of Cambridge)
Philippe Schwaller (EPFL)
Jian Tang (Mila)

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