Bayesian illumination: Inference and quality-diversity accelerate generative molecular models

In recent years, there have been considerable academic and industrial research efforts to develop novel generative models for high-performing, small molecules. Traditional, rules-based algorithms such as genetic algorithms have, however, been shown to rival deep learning approaches in terms of both efficiency and potency [Jensen, Chem. Sci., 2019, 12, 3567-3572]. In previous work, we showed that the addition of a quality-diversity archive to a genetic algorithm resolves stagnation issues and substantially increases search efficiency [Verhellen, Chem. Sci., 2020, 42, 11485-11491]. In this work, we expand on these insights and leverage the availability of bespoke kernels for small molecules [Griffiths, Adv. Neural. Inf. Process. Syst., 2024, 36] to integrate Bayesian optimisation into the quality-diversity process. This novel generative model, which we call Bayesian Illumination, produces a larger diversity of high-performing molecules than standard quality-diversity optimisation methods. In addition, we show that Bayesian Illumination further improves search efficiency compared to previous generative models for small molecules, including deep learning approaches, genetic algorithms, and standard quality-diversity methods.