发表机构
Stony Brook University; Novo Nordisk（石溪大学; 诺和诺德公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出短期图记忆模块，用于在有限神谕预算下优化分子，可提升生成器的前10名分数，且适用于多款生成器，为合理分配神谕预算提供了方法。
AI 中文摘要
分子优化通常在有限的神谕预算下进行，这使得决定评估什么与决定生成什么同样重要。我们引入短期图记忆（short-term graph memory），这是一种即插即用模块，在保留生成器架构和原生更新规则的同时，从先前评估的分子中学习以优先处理后续的神谕查询。该模块维护一个在线图神经代理，对每一轮的候选池进行预筛选，从而将固定的神谕预算用于具有更高预测效用的分子。将其应用于标准分子优化基准上的基于片段的生成器，它在不增加神谕成本的情况下提高了平均前10名分数，并且在任何神谕下都从未落后于基准；在1000次调用的紧张预算下，其增益扩展到我们测试的所有四个生成器。随后我们分析了代理引导选择如何与不同生成器的探索和利用行为相互作用。其在更大预算下的益处与主干的两个属性一致：搜索的广度以及其原生搜索利用神谕反馈的有效性。我们提供了一种更有选择性地使用固定神谕预算的简单方法，并给出了哪些生成器从中受益的证据。
英文摘要
Molecular optimization is commonly performed under a limited oracle budget, which makes deciding what to evaluate as important as deciding what to generate. We introduce short-term graph memory, a plug-in module that preserves the generator architecture and native update rule while learning from previously evaluated molecules to prioritize subsequent oracle queries. The module maintains an online graph neural surrogate that pre-screens each round's candidate pool, so the fixed oracle budget is spent on molecules with higher predicted utility. Applied to a fragment-based generator on a standard molecular optimization benchmark, it improves the mean top-10 score at no extra oracle cost and never falls behind the base on any oracle; the gain extends to all four generators we tested at a tight budget of one thousand calls. We then analyze how surrogate-guided selection interacts with the exploration and exploitation behavior of different generators. Its benefit at larger budgets is consistent with two properties of the backbone: how broadly it searches, and how effectively its native search already exploits oracle feedback. We provide a simple way to spend a fixed oracle budget more selectively, and evidence on which generators benefit from it.
Comments12 pages, 5 figures
