发表机构
School of Data Science, The Chinese University of Hong Kong, Shenzhen; Shanghai Artificial Intelligence Laboratory（香港中文大学（深圳）数据科学学院; 上海人工智能实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出基于语义大语言模型代理的闭环贝叶斯分子逆设计框架，在MolQA任务上优于一次性提示，与GP-based BO基线相当或更优，且揭示了领域依赖的参考转移与摘要添加的接口
AI 中文摘要
实用的分子逆设计很少是一次性生成问题，通常表现为闭环候选池富集，即在有限的oracle预算下，目标是提高生成的分子中符合期望属性谱的比例。贝叶斯优化（BO）为此场景提供了自然框架，但标准高斯过程代理通常在压缩的连续嵌入中运行，会丢失化学家自然用于决定下一步研究方向的子结构和参考相似性信号。我们提出了\textbf{\name}，这是一个闭环框架，其中代理而非生成器被视为设计选择的核心，我们用一个冻结的大语言模型实例化该框架，该模型直接基于任务指令、SMILES级优化历史和oracle反馈的原始文本形式进行推理。在每次迭代中，代理返回结构化决策信号，该信号在探索与利用原则下选择信息丰富的参考分子，可选附带简洁的指导语句。该信号被转换为下一轮冻结分子生成器的条件文本，从而产生可检查的自然语言优化轨迹。在MolQA药物和材料设计任务上的实验表明，\name优于一次性提示，与基于GP的BO基线相比具有竞争力或更强，且揭示了依赖领域的接口：仅参考转移对二元药物靶点效果最佳，而添加简洁的代理摘要对连续材料设计更有益
英文摘要
Practical molecular inverse design is rarely a one-shot generation problem; it often takes the form of closed-loop candidate-pool enrichment, where under a limited oracle budget the goal is to increase the fraction of generated molecules that match a desired property profile. Bayesian optimization (BO) offers a natural framework for this setting, yet standard Gaussian-process surrogates typically operate in compressed continuous embeddings, which discard the substructural and reference-similarity signals that chemists naturally use to decide where to look next. We propose BoMolLLM, a closed-loop framework in which the surrogate, rather than the generator, is treated as the locus of design choice, and instantiate it with a frozen large language model that reasons directly over the task instruction, SMILES-level optimization history, and oracle feedback in their native textual form. At each iteration, the surrogate returns a structured decision signal that selects informative reference molecules under an exploration and exploitation principle, optionally with a concise guidance sentence. This signal is converted into next-round conditioning text for a frozen molecular generator, yielding an inspectable optimization trace in natural language. Experiments on MolQA drug and material design tasks show that BoMolLLM improves over one-shot prompting, is competitive with or stronger than GP-based BO baselines, and reveals a domain-dependent interface: reference-only transfer works best for binary drug targets, while adding a concise surrogate summary is more beneficial for continuous material
Comments28 pages
