首次将分子生成中的创造力作为核心能力进行系统研究。
How Creative Are Large Language Models in Generating Molecules?
- 从收敛与发散两个维度评估大模型的创造性行为。
- 发现增加约束能提升模型满足化学约束的能力。
- 为药物发现中如何使用大模型提供明确指导。
分子生成需在庞大的结构化化学空间中满足多重物理化学、ADMET及生物活性约束，属于非二元问题。有效模型需在约束下识别非显性解，同时保持探索性以跳出局部最优。因此，创造力是分子生成的功能需求而非美学概念。大语言模型（LLMs）可直接从自然语言提示生成分子表示，但其在该任务中的创造力类型及其评估方式尚不明确。本文通过系统实证评估，涵盖理化性质、ADMET及生物活性任务，刻画了收敛性创造力与发散性创造力两个互补维度，并分析不同因素对行为的影响。结果表明，LLMs在分子生成中表现出独特的创造性模式，例如在引入更多约束时，其满足约束的能力显著提升。本工作首次将分子生成所需能力重构为创造力，系统揭示了基于大模型的分子生成中的创造力机制，明确了大模型在分子发现流程中的合理应用方式。
原文摘要 · Abstract (English)
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.
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