Year: 2025
Type: article
Abstract: Batched Bayesian optimization (BO) can accelerate molecular design by efficiently identifying top-performing compounds from a large chemical library. Existing acquisition strategies for batch design in BO aim to balance exploration and exploitation. This often involves optimizing nonadditive batch acquisition functions, necessitating approximation via myopic construction and/or diversity heuristics. In this work, we propose an acquisition strategy for discrete optimization that is motivated by pure exploitation, qPO (multipoint Probability of Optimality). qPO maximizes the probability that the batch includes the true optimum, which is expressed as the sum over individual acquisition scores and thereby circumvents the combinatorial challenge of optimizing a batch acquisition function. We differentiate the proposed strategy from parallel Thompson sampling and discuss how it implicitly captures diversity. Finally, we apply our method to the model-guided exploration of large chemical libraries and provide empirical evidence that it is competitive with and complements other state-of-the-art methods in batched Bayesian optimization.
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
Funders: National Science Foundation, Massachusetts Institute of Technology, Alan Turing Institute, National Institutes of Health, Office of Naval Research, National Institute of General Medical Sciences, U.S. Naval Research Laboratory, National Science Foundation Graduate Research Fellowship Program
OpenAlex ID: W4410086330