Can Large Language Models Translate MS/MS into Molecular Caption?
Mass spectrometry (MS) is central to molecular discovery, yet the interpretation of tandem mass spectra (MS/MS) remains limited by database dependence and an incomplete structural resolution. Here, we explore whether large language models (LLMs) can directly translate MS/MS spectra to chemically meaningful molecular descriptions. We introduce MS2LLM, a framework that represents spectra and molecular structures as natural languages and learns their correspondence through instruction tuning. MS2LLM generates hierarchical molecular descriptions, including functional groups, substructures, and chemical classes, directly from the spectral input. Across multiple datasets, it outperforms general-purpose LLMs and conventional spectral learning methods in both descriptive accuracy and chemical classification. Importantly, the model produces interpretable outputs that capture structural semantics rather than exact structures, offering a complementary paradigm for structural inference of unknowns.