Gary Tom — accepted AI-conference papers

2023

GAUCHE: A Library for Gaussian Processes in Chemistry

Ryan-Rhys Griffiths, Leo Klarner, Henry Moss, Aditya Ravuri, Sang T. Truong, Yuanqi Du +22

NeurIPS 2023poster

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 repr…

Cited by 37 Source ↗PDF ↗code ↗Scholar ↗

2023

Tartarus: A Benchmarking Platform for Realistic And Practical Inverse Molecular Design

AkshatKumar Nigam, Robert Pollice, Gary Tom, Kjell Jorner, John Willes, Luca Thiede +2

NeurIPS 2023poster

The efficient exploration of chemical space to design molecules with intended properties enables the accelerated discovery of drugs, materials, and catalysts, and is one of the most important outstanding challenges in chemistry. Encouraged by the recent surge in computer power and artificial intelli…

Cited by 36 Source ↗PDF ↗code ↗Scholar ↗