Poster
in
Workshop: AI for Science: Scaling in AI for Scientific Discovery
Basic Bayesian Optimization is Underrated for Molecule Design
Austin Tripp · Jose Miguel Hernandez-Lobato
Keywords: [ chemistry ] [ Bayesian Optimization ] [ Molecule ]
Recently, the most popular algorithms for molecule design are based on techniques like reinforcement learning, which explore randomly. This paper asks whether Bayesian optimization (BO), a technique which explores deliberately, can do better. We explain some common pitfalls of BO and how to address them. Notably, we show that a correctly-tuned basic BO setup is able to achieve the highest overall performance on the PMO benchmark for molecule design (Gae et al, 2022). We end with a discussion of the strengths and limitations of BO, arguing why it is overall underrated by the ML community.