PropertyDAG：生物序列设计中半序混合变量特性的多目标贝叶斯优化
PropertyDAG: Multi-objective Bayesian optimization of partially ordered, mixed-variable properties for biological sequence design
摘要：
贝叶斯优化提供了一个样本效率的框架，用于在生物序列的巨大设计空间中导航探索-利用权衡。尽管可以使用多目标采集函数（例如预期超容积改善（EHVI））来联合优化各种感兴趣的属性，但是该方法不考虑具有分层依赖性结构的目标。我们考虑一个常见的用例，其中根据目标中指定的$\textit{partial ordering}$，帕累托边界的某些区域优先于其他区域。例如，当设计抗体时，我们希望只有当靶抗原可以在活细胞培养物中表达时才能最大化与靶抗原的结合亲和力-模拟实验依赖性，其中亲和力只能针对可以表达并因此以可行量产生的抗体进行测量。一般来说，我们可能希望赋予属性一个偏序，使得每个属性在其父属性满足某些可行性条件的条件下进行优化。为此，我们提出了PropertyDAG，一个框架，该框架在传统的多目标BO之上操作，以将这种期望的排序强加于目标，例如表达式$\rightarrow$亲和力。我们证明了它的性能在多个模拟的主动学习迭代青霉素生产任务，玩具数值问题，和现实世界的抗体设计任务。
英文摘要：
Bayesian optimization offers a sample-efficient framework for navigating the exploration-exploitation trade-off in the vast design space of biological sequences. Whereas it is possible to optimize the various properties of interest jointly using a multi-objective acquisition function, such as the expected hypervolume improvement (EHVI), this approach does not account for objectives with a hierarchical dependency structure. We consider a common use case where some regions of the Pareto frontier are prioritized over others according to a specified $\textit{partial ordering}$ in the objectives. For instance, when designing antibodies, we would like to maximize the binding affinity to a target antigen only if it can be expressed in live cell culture -- modeling the experimental dependency in which affinity can only be measured for antibodies that can be expressed and thus produced in viable quantities. In general, we may want to confer a partial ordering to the properties such that each property is optimized conditioned on its parent properties satisfying some feasibility condition. To this end, we present PropertyDAG, a framework that operates on top of the traditional multi-objective BO to impose this desired ordering on the objectives, e.g. expression $\rightarrow$ affinity. We demonstrate its performance over multiple simulated active learning iterations on a penicillin production task, toy numerical problem, and a real-world antibody design task.
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