用大模型统一分析多种光谱数据，自动推断分子结构
SpectraLLM: Uncovering the Ability of LLMs for Molecular Structure Elucidation from Multi-Spectral Data
- 将不同光谱类型映射到统一语言空间，联合推理分子结构
- 在4个公开数据集上超越单模态基线，多谱联合提升准确率
- 适合需要多模态光谱分析的化学与药物研发人员
自动化分子结构解析仍具挑战，现有方法常依赖预编数据库或仅限单一光谱模态。本文提出SpectraLLM，一种可端到端预测结构的大语言模型，能基于一种或多种光谱进行推理。不同于传统谱图到结构的流程，SpectraLLM将连续（IR、Raman、UV-Vis、NMR）与离散（MS）模态统一表示于共享语言空间，捕捉跨谱型的互补子结构模式。模型在小分子领域预训练并微调，评估覆盖四个公开基准数据集。SpectraLLM实现当前最优性能，显著优于单模态基线；且在单模态设置下仍具强鲁棒性，多谱联合推理进一步提升准确率，建立可扩展的语言驱动光谱分析范式。代码已开源：https://github.com/OPilgrim/SpectraLLM。
原文摘要 · Abstract (English)
Automated molecular structure elucidation remains challenging, as existing approaches often depend on pre-compiled databases or restrict themselves to single spectroscopic modalities. Here we introduce SpectraLLM, a large language model that performs end-to-end structure prediction by reasoning over one or multiple spectra. Unlike conventional spectrum-to-structure pipelines, SpectraLLM represents both continuous (IR, Raman, UV-Vis, NMR) and discrete (MS) modalities in a shared language space, enabling it to capture substructural patterns that are complementary across different spectral types. We pretrain and fine-tune the model on small-molecule domains and evaluate it on four public benchmark datasets. SpectraLLM achieves state-of-the-art performance, substantially surpassing single-modality baselines. Moreover, it demonstrates strong robustness in unimodal settings and further improves prediction accuracy when jointly reasoning over diverse spectra, establishing a scalable paradigm for language-based spectroscopic analysis. Code is available at https://github.com/OPilgrim/SpectraLLM.
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