Adaptive Modeling of Tandem Mass Spectrometry Data: Creation of the METLIN 960K MRM Database.

Multiple Reaction Monitoring (MRM) remains the gold standard for quantitative mass spectrometry but continues to be constrained by the limited availability of high-quality transitions and collision energy (CE) values for many biologically and chemically relevant molecules. Here, we present the METLIN 960K MRM library, a 960,000-compound transition resource derived entirely from empirically acquired MS/MS data. MRM transitions were generated in both positive and negative ionization modes using an empirical spline-based pipeline refined by AI BioSync, an XCMS enhancement that provides a framework of AI and machine-learning tools designed to decipher spectral data for biological and analytical relevance. Central to this approach is spline fitting of CE-dependent intensity profiles from experimental MS/MS data collected at four discrete energies (0, 10, 20, and 40 eV), enabling continuous CE modeling and precise prediction of optimal fragmentation conditions. Supervised learning models were used within AI BioSync to refine spline fitting across diverse chemical classes, improving reproducibility and predictive accuracy. Validation across more than 100 authentic compounds, including rare metabolites and diverse small molecules, demonstrated robust detection down to 1 nM, confirming both sensitivity and scalability. This framework also holds immediate applicability for preclinical drug development studies, where authentic metabolite and impurity standards are often unavailable. Unlike prior methods reliant on