离线大语言模型如何通过链式推理解析质谱中的分子结构？
How well can off-the-shelf LLMs elucidate molecular structures from mass spectra using chain-of-thought reasoning?
AI总结：
本研究评估了离线大语言模型通过链式推理解析质谱数据的能力，发现其在化学准确性上存在局限。
摘要：
质谱分析（MS）是一种用于鉴定小分子的强大分析技术，但直接从串联质谱（MS/MS）确定完整的分子结构仍是一个长期存在的挑战，原因在于复杂的碎片化模式和化学空间的广泛多样性。近年来，大型语言模型（LLMs）在需要推理的科学任务中显示出潜力，但它们在化学解释方面的能力仍不明确。在本工作中，我们引入了一个链式推理（CoT）提示框架和评估基准，以评估LLMs如何推理质谱数据以预测分子结构。我们将专家化学家的推理步骤——如双键等价（DBE）分析、中性损失识别和碎片组装——转化为结构化的提示，并在零样本设置下使用MassSpecGym数据集评估多个最先进的LLMs（Claude-3.5-Sonnet、GPT-4o-mini和Llama-3系列）。我们的评估在SMILES有效性、公式一致性和结构相似性等指标上显示，尽管LLMs可以生成语法有效且部分合理的结构，但它们无法实现化学准确性或将推理与正确的分子预测联系起来。这些发现突显了基于LLMs的推理在分子解析中的潜力和当前的局限性，为未来结合领域知识和强化学习以实现化学基础的AI推理奠定了基础。
英文摘要：
Mass spectrometry (MS) is a powerful analytical technique for identifying small molecules, yet determining complete molecular structures directly from tandem mass spectra (MS/MS) remains a long-standing challenge due to complex fragmentation patterns and the vast diversity of chemical space. Recent progress in large language models (LLMs) has shown promise for reasoning-intensive scientific tasks, but their capability for chemical interpretation is still unclear. In this work, we introduce a Chain-of-Thought (CoT) prompting framework and benchmark that evaluate how LLMs reason about mass spectral data to predict molecular structures. We formalize expert chemists' reasoning steps-such as double bond equivalent (DBE) analysis, neutral loss identification, and fragment assembly-into structured prompts and assess multiple state-of-the-art LLMs (Claude-3.5-Sonnet, GPT-4o-mini, and Llama-3 series) in a zero-shot setting using the MassSpecGym dataset. Our evaluation across metrics of SMILES validity, formula consistency, and structural similarity reveals that while LLMs can produce syntactically valid and partially plausible structures, they fail to achieve chemical accuracy or link reasoning to correct molecular predictions. These findings highlight both the interpretive potential and the current limitations of LLM-based reasoning for molecular elucidation, providing a foundation for future work that combines domain knowledge and reinforcement learning to achieve chemically grounded AI reasoning.
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