How Creative Are Large Language Models in Generating Molecules?

Molecule generation requires satisfying chemical and biological constraints while searching a large and structured chemical space. This makes molecule generation a non-binary problem: many distinct molecular structures can satisfy the same constraints, and effective models must identify non-obvious solutions while maintaining exploration. 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 kind of creativity they exhibit when generating molecules. Through a systematic evaluation of 10 general-purpose LLMs across 15 molecular generation tasks, we study creativity along two complementary dimensions: convergent creativity, which captures validity and constraint satisfaction, and divergent creativity, which captures novelty, uniqueness, and structural exploration. Our central finding is that higher convergent creativity is consistently associated with lower divergent creativity: the relationship is negative within all 15 evaluation tasks. We next examine how molecular creativity varies with three factors: task structure, model family, and inference-time conditioning. In the tested compatible multi-constraint tasks, adding constraints does not necessarily reduce success; across size-varied checkpoints, the LLaMA families gain convergent creativity while losing divergent creativity, whereas Qwen3 improves both; and in-context demonstrations can sharply increase activity-task success while reducing exploration and directly reproducing supplied examples. We further test whether creativity enhanced in language transfers to molecular generation. In a controlled same-parent comparison, the three creativity-oriented variants fail to reproduce their targeted molecular gains.