用短期图记忆提升分子优化效率，不增加预算也能更好选候选分子。
Oracle-Budgeted Molecular Optimization with Short-Term Graph Memory
- 引入短期图记忆模块，基于已评估分子优化后续筛选策略。
- 在1000次调用预算下，所有4种生成器的前10名平均得分均提升。
- 适合需要高效利用有限实验资源的药物分子生成任务。
分子优化通常受限于有限的实验预算（oracle budget），因此决定评估什么比生成什么更重要。本文提出短时图记忆（short-term graph memory），一个可插拔模块，在不改变生成器架构和更新规则的前提下，通过学习历史评估结果来优先选择后续查询的分子。该模块维护一个在线图神经网络代理，对每轮候选分子池进行预筛选，使固定预算集中于预测价值更高的分子。在标准分子优化基准上应用于片段生成器，实现无需额外预算的情况下平均前10名得分提升，且在任何预算点均不落后于基线；在1000次调用的紧凑预算下，四种生成器均获益。进一步分析表明，代理引导选择的效果与生成器的探索-利用行为相关：其收益大小取决于生成器的搜索广度及其对反馈利用的有效性。本文提供一种更精准分配固定预算的方法，并给出哪些生成器最受益的实证证据。
原文摘要 · Abstract (English)
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.
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