这篇论文教你怎么用图神经网络当筛选器，同样的查询次数分子优化效果更好，四个生成器都试了，值得看。
该论文提出短时图记忆模块，在固定oracle预算下更高效地筛选候选分子。模块用在线图神经网络预筛每轮候选池，将有限查询次数留给预测效用更高的分子。在标准分子优化基准上，该方法平均top-10分数提升且无额外oracle成本。在1000次调用的紧凑预算下，全部四个测试生成器均有收益。论文还分析了代理引导选择与各生成器探索/利用行为的关系。
Oracle-Budgeted Molecular Optimization with Short-Term Graph Memory
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.