Liquid chromatography tandem mass spectrometry (LC-MS/MS) is a critical analytical technique for molecular identification across metabolomics, environmental chemistry, and chemical forensics. A variety of computational methods have emerged for structural annotation of spectral features of interest, but many of these features cannot be confidently annotated with reference structures or spectra. Here, we introduce FOAM (Formula-constrained Optimization for Annotating Metabolites), a computational workflow that poses structure elucidation from LC-MS/MS as an iterative optimization problem. FOAM couples a formula-constrained graph genetic algorithm with spectral simulation to explore candidate annotations given an experimental spectrum. We demonstrate FOAM's performance on the NIST'20 and MassSpecGym datasets as both a standalone elucidation pipeline and as a complement to existing inverse models. This work establishes iterative optimization as an effective and extensible paradigm for structural elucidation.

翻译：液相色谱串联质谱（LC-MS/MS）是代谢组学、环境化学和化学法医学领域分子鉴定的关键分析技术。目前已有多种计算方法用于对目标谱峰特征进行结构注释，但其中许多特征无法通过参考结构或参考谱图进行可靠注释。本文提出FOAM（代谢物注释的分子式约束优化方法），这是一种将LC-MS/MS数据解析转化为迭代优化问题的计算流程。FOAM通过耦合分子式约束的图遗传算法与谱图模拟技术，在给定实验谱图条件下探索候选注释结构。我们在NIST'20和MassSpecGym数据集上验证了FOAM作为独立解析流程及现有逆向模型补充工具的性能。本研究表明，迭代优化是结构解析中一种有效且可扩展的研究范式。