Diagnosing and fixing common problems in Bayesian optimization for molecule design

Published: 17 Jun 2024, Last Modified: 25 Jul 2024ICML2024-AI4Science PosterEveryoneRevisionsBibTeXCC BY 4.0
Keywords: Bayesian optimization, molecule, chemistry
TL;DR: Basic Bayesian optimization works better than people think, *if* hyperparameters are tuned correctly
Abstract: Bayesian optimization (BO) is a principled approach to molecular design tasks. In this paper we explain three pitfalls of BO which can cause poor empirical performance: an incorrect prior width, over-smoothing, and inadequate acquisition function maximization. We show that with these issues addressed, even a basic BO setup is able to achieve the highest overall performance on the PMO benchmark for molecule design (Gao et al 2022). These results suggest that BO may benefit from more attention in the machine learning for molecules community.
Submission Number: 144
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