Many crucial scientific problems involve designing novel molecules with desired properties, which can be formulated as an expensive black-box optimization problem over the discrete chemical space. Computational methods have achieved initial success but still struggle with simultaneously optimizing multiple competing properties in a sample-efficient manner. In this work, we propose a multi-objective Bayesian optimization (MOBO) algorithm leveraging the hypernetwork-based GFlowNets (HN-GFN) as an acquisition function optimizer, with the purpose of sampling a diverse batch of candidate molecular graphs from an approximate Pareto front. Using a single preference-conditioned hypernetwork, HN-GFN learns to explore various trade-offs between objectives. Inspired by reinforcement learning, we further propose a hindsight-like off-policy strategy to share high-performing molecules among different preferences in order to speed up learning for HN-GFN. Through synthetic experiments, we illustrate that HN-GFN has adequate capacity to generalize over preferences. Extensive experiments show that our framework outperforms the best baselines by a large margin in terms of hypervolume in various real-world MOBO settings.

翻译：许多关键科学问题涉及设计具有所需性质的新型分子，这可以形式化为离散化学空间上的昂贵黑箱优化问题。计算方法已取得初步成功，但在以样本高效方式同时优化多个相互竞争性质方面仍面临挑战。本文提出一种多目标贝叶斯优化算法，利用基于超网络的GFlowNets作为采集函数优化器，旨在从近似帕累托前沿中采样多样化的候选分子图批次。通过单一偏好条件超网络，HN-GFN学习探索目标间的各种权衡。受强化学习启发，我们进一步提出一种事后型离策略方法，在不同偏好间共享高性能分子，以加速HN-GFN的学习。通过合成实验，我们证明了HN-GFN在偏好泛化方面具有充分能力。大量实验表明，在多种实际多目标贝叶斯优化场景中，我们的框架在超体积指标上大幅优于最先进的基线方法。