利用离散函数分解进行科学设计
Leveraging Discrete Function Decomposability for Scientific Design
AI总结：
本文提出DADO算法，通过利用设计变量的分解结构，提升离散空间优化效率，用于科学设计问题。
摘要：
在人工智能驱动的科学和工程时代，我们常常希望根据用户指定的属性来设计离散对象。例如，我们可能希望设计一种能够与其靶点结合的蛋白质，安排电路中的组件以最小化延迟，或寻找具有特定性质的材料。给定一个属性预测模型，计算机内设计通常涉及在设计空间（例如蛋白质序列空间）上训练生成模型，以集中于具有所需属性的设计。分布优化——可以形式化为分布估计算法或强化学习策略优化——找到最大化期望目标函数的生成模型。在离散值设计上优化分布通常是具有挑战性的，因为设计空间具有组合性质。然而，许多科学应用中的属性预测器是可分解的，即它们可以以某种方式在设计变量上分解，原则上可以更有效地优化。例如，蛋白质催化位点的氨基酸可能只与蛋白质其余部分的氨基酸松散相互作用，以达到最大催化活性。当前的分布优化算法无法利用这种可分解性结构。在此，我们提出并展示了新的分布优化算法，即分解感知分布优化（DADO），它可以利用由junction tree定义的任何可分解性，使优化更加高效。其核心是采用一种软因子化的“搜索分布”——一个学习的生成模型——以高效导航搜索空间，通过图消息传递协调跨相关因子的优化。
英文摘要：
In the era of AI-driven science and engineering, we often want to design discrete objects in silico according to user-specified properties. For example, we may wish to design a protein to bind its target, arrange components within a circuit to minimize latency, or find materials with certain properties. Given a property predictive model, in silico design typically involves training a generative model over the design space (e.g., protein sequence space) to concentrate on designs with the desired properties. Distributional optimization$\unicode{x2013}$which can be formalized as an estimation of distribution algorithm or as reinforcement learning policy optimization$\unicode{x2013}$finds the generative model that maximizes an objective function in expectation. Optimizing a distribution over discrete-valued designs is in general challenging because of the combinatorial nature of the design space. However, many property predictors in scientific applications are decomposable in the sense that they can be factorized over design variables in a way that could in principle enable more effective optimization. For example, amino acids at a catalytic site of a protein may only loosely interact with amino acids of the rest of the protein to achieve maximal catalytic activity. Current distributional optimization algorithms are unable to make use of such decomposability structure. Herein, we propose and demonstrate use of a new distributional optimization algorithm, Decomposition-Aware Distributional Optimization (DADO), that can leverage any decomposability defined by a junction tree on the design variables, to make optimization more efficient. At its core, DADO employs a soft-factorized "search distribution"$\unicode{x2013}$a learned generative model$\unicode{x2013}$for efficient navigation of the search space, invoking graph message-passing to coordinate optimization across linked factors.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
