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arXiv 2604.18031cs.CLcs.LGq-bio.BM

大语言模型在生成分子时的创造性如何?

How Creative Are Large Language Models in Generating Molecules?

Nanyang Technological University(南洋理工大学);University of California, Merced(加州大学默塞德分校);Hunan University(湖南大学);National University of Singapore(新加坡国立大学)

Wen Tao, Yiwei Wang, Peng Zhou, Bryan Hooi, Wanlong Fang, Tianle Zhang, Xiao Luo, Yuansheng Liu, Alvin Chan

AI总结:

研究通过系统实证评估,分析大语言模型在分子生成中的创造性行为,揭示其在不同约束下的表现,首次将分子生成能力重新定义为创造性。

摘要:

分子生成需要在满足多种化学和生物学约束的同时,在庞大的结构化学空间中进行搜索。这使其成为一个非二元问题,有效的模型必须在约束下识别非显而易见的解决方案,同时保持探索以通过逃离局部最优来提高成功率。从这个角度来看,创造力是分子生成中的功能性要求,而非一种美学概念。大型语言模型(LLMs)可以直接从自然语言提示生成分子表示,但尚不清楚它们在这一设定中表现出何种类型的创造力以及如何评估。在本工作中,我们通过跨物理化学、 ADMET 和生物活性任务的系统实证评估,研究了 LLMs 在分子生成中的创造性行为。我们沿着两个互补的维度,即收敛创造力和发散创造力,来刻画创造力,并分析不同因素如何塑造这些行为。我们的结果表明,LLMs 在分子生成中表现出不同的创造性行为模式,例如在增加额外约束时,约束满足度的增加。总体而言,我们的工作首次将分子生成所需的能力重新定义为创造力,为基于 LLM 的分子生成中的创造力提供了系统理解,并明确了 LLMs 在分子发现流程中的适当应用。

英文摘要:

Molecule generation requires satisfying multiple chemical and biological constraints while searching a large and structured chemical space. This makes it a non-binary problem, where effective models must identify non-obvious solutions under constraints while maintaining exploration to improve success by escaping local optima. From this perspective, creativity is a functional requirement in molecular generation rather than an aesthetic notion. Large language models (LLMs) can generate molecular representations directly from natural language prompts, but it remains unclear what type of creativity they exhibit in this setting and how it should be evaluated. In this work, we study the creative behavior of LLMs in molecular generation through a systematic empirical evaluation across physicochemical, ADMET, and biological activity tasks. We characterize creativity along two complementary dimensions, convergent creativity and divergent creativity, and analyze how different factors shape these behaviors. Our results indicate that LLMs exhibit distinct patterns of creative behavior in molecule generation, such as an increase in constraint satisfaction when additional constraints are imposed. Overall, our work is the first to reframe the abilities required for molecule generation as creativity, providing a systematic understanding of creativity in LLM-based molecular generation and clarifying the appropriate use of LLMs in molecular discovery pipelines.

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。

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