Fromer Jenna, Wang Runzhong, Manjrekar Mrunali, Tripp Austin, Hernández-Lobato José Miguel, Coley Connor W
Department of Chemical Engineering, MIT, Cambridge, Massachusetts 02139, United States.
Department of Engineering, University of Cambridge, Cambridge CB2 1CB2 1PZ, U.K.
J Chem Inf Model. 2025 May 26;65(10):4808-4817. doi: 10.1021/acs.jcim.5c00214. Epub 2025 May 5.
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.
批量贝叶斯优化（BO）可以通过从大型化学库中高效识别性能最佳的化合物来加速分子设计。现有的用于BO中批量设计的采集策略旨在平衡探索和利用。这通常涉及优化非加性批量采集函数，需要通过近视构建和/或多样性启发式方法进行近似。在这项工作中，我们提出了一种用于离散优化的采集策略，其动机是纯利用，即qPO（多点最优概率）。qPO最大化了批次包含真正最优解的概率，该概率表示为各个采集分数的总和，从而规避了优化批量采集函数的组合挑战。我们将所提出的策略与并行汤普森采样区分开来，并讨论它如何隐含地捕捉多样性。最后，我们将我们的方法应用于大型化学库的模型引导探索，并提供经验证据表明它在批量贝叶斯优化中与其他现有方法具有竞争力且相互补充。
