AI 中文总结
研究针对NMR数据结构解析这一化学等领域的瓶颈，构建由冻结语言模型支持的自主智能体，将其解析过程视为受限搜索而非建模任务，在多个数据集上取得良好结果，为自动化光谱分析的多步骤编排框架提供了方向。
AI 中文摘要
核磁共振（NMR）数据的结构解析在化学、材料科学和生物学领域仍是一个基本瓶颈。我们证明一个智能体人工智能系统能以与研究生水平化学学生相当的水平执行此任务。我们构建了一个由冻结的语言模型支持的自主智能体，它与精心策划的环境交互，可使用特定领域处理工具、验证检查、化学位移表和概述化学家思维过程逐步性质的指令，而非训练模型直接将光谱映射到结构。在阿尔伯茨数据集上，我们的智能体以71%的top-1准确率解析结构，与研究生66%的top-1准确率相当。在范布拉默和阿斯利康数据集上，分别达到80%和20%的top-1准确率，优于在模拟光谱大数据集上训练的零样本端到端深度学习模型。这些结果表明将NMR解析重新构建为语言模型引导的受限搜索而非建模任务能带来显著收益，并为多步骤编排框架指明方向，该框架整合各种工具、模型和领域知识以协助自动化光谱分析。
英文摘要
Structural elucidation from Nuclear Magnetic Resonance (NMR) data remains a fundamental bottleneck across chemistry, materials science, and biology. We demonstrate that an agentic AI system can perform this task at a level comparable to graduate-level chemistry students. Instead of training a model to directly map spectra to structures, we build a single autonomous agent, backed by a frozen LLM, that interacts with a curated environment with access to domain-specific processing tools, validation checks, tabulated chemical shifts, and instructions that outline the stepwise nature of a chemist's thinking process. On the Alberts dataset, our agent elucidates structures with a top-1 accuracy of 71%, comparable to the performance of graduate students at 66% top-1 accuracy. On the van Bramer and AstraZeneca datasets, our agent achieved 80% and 20% top-1 accuracy respectively, outperforming zero-shot end-to-end deep learning models which were trained on large datasets of simulated spectra. These results show that reframing NMR elucidation as an LLM-guided constrained search, rather than a modeling task, yields substantial gains and suggests a path toward multi-step orchestration frameworks that integrate a variety of tools, models, and domain knowledge to assist in automating spectroscopic analysis.
