用红外和核磁谱图直接生成分子结构，无需专家经验
NMIRacle: Multi-modal Generative Molecular Elucidation from IR and NMR Spectra
- 分两阶段生成：先重构片段，再结合谱图条件生成分子
- 在复杂分子上仍保持高准确率，优于现有方法
- 适合化学合成与药物研发人员快速解析分子结构
从光谱数据中进行分子结构解析是化学领域长期存在的挑战，传统方法依赖专家解读。我们提出NMIRacle，一种两阶段生成式框架，基于最新AI驱动光谱学范式，假设极少。第一阶段，NMIRacle学习从计数感知的片段表示中重建分子结构，同时捕捉片段身份及其出现次数。第二阶段，通过光谱编码器将输入谱图（红外、¹H-NMR、¹³C-NMR）映射为潜在嵌入，用于条件化预训练生成器，该生成器经微调后可实现直接谱图到分子的生成。该方法融合片段级化学建模与谱图证据，实现精准分子预测。实验结果表明，NMIRacle在分子解析任务中超越现有基线，在分子复杂度提升时仍保持稳健性能。
原文摘要 · Abstract (English)
Molecular structure elucidation from spectroscopic data is a long-standing challenge in Chemistry, traditionally requiring expert interpretation. We introduce NMIRacle, a two-stage generative framework that builds upon recent paradigms in AI-driven spectroscopy with minimal assumptions. In the first stage, NMIRacle learns to reconstruct molecular structures from count-aware fragment representations, capturing both fragment identities and their occurrences. In the second stage, a spectral encoder maps input spectra (IR, 1H-NMR, 13C-NMR) into a latent embedding used to condition the pre-trained generator, which is fine-tuned for direct spectra-to-molecule generation. This formulation bridges fragment-level chemical modeling with spectral evidence, yielding accurate molecular predictions. Empirical results demonstrate that NMIRacle outperforms existing baselines on molecular elucidation, while maintaining robust performance across increasing levels of molecular complexity.
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