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METLIN 960 K represents the largest collection of experimentally acquired small-molecule MS/MS spectra currently available. We introduce a reengineered publicaly accessible METLIN platform integrating high-resolution tandem mass spectrometry (MS/MS) data for over 960,000 empirically validated molecular standards. This scale was enabled by a high-throughput experimental framework integrating acoustic droplet ejection with high-throughput LC-MS/MS acquisition, allowing systematic empirical generation of MS/MS spectra from authentic standards. In addition to scale, METLIN 960 K provides a uniquely standardized MS/MS data set, with spectra acquired under controlled and consistent conditions across ionization modes and collision energies, enabling reproducible spectral comparison and machine-learning applications. Each compound is characterized by MS/MS spectra acquired in both positive and negative ionization modes across four collision energies (0, 10, 20, and 40 eV), enabling comprehensive fragmentation coverage and improved structural annotation. Designed as a reference library for XCMS-METLIN and compatible with machine-learning workflows, METLIN 960 K supports high-fidelity spectral matching, neutral loss analysis, and filtering of misannotations, including annotation of in-source fragments and biologically synchronized ranking of candidate metabolites. The platform also provides empirically derived MRM transitions on all standards (via METLIN-MRM), supporting quantitative method development across a chemically diverse range of metabolites, natural products, lipids, peptides, pharmaceuticals, and toxicants. A redesigned interface enables efficient querying by exact mass, formula, or structure with direct access to curated spectra and metadata. Two additional resources enhance identification: (1) METLIN Core, a high-frequency-use subset for rapid searching, and (2) > 1.02 million additional structures without MS/MS data for hypothesis generation. Derived exclusively from authentic standards, METLIN 960 K (https://metlin.scripps.edu) provides the largest publicly available empirical MS/MS database, delivering high-confidence annotation for both untargeted and targeted mass spectrometry workflows.

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 in silico fragmentation or heuristic rules, all transitions are derived directly from experimental MS/MS data using absolute intensities. The resulting precursor m/z-centric METLIN 960K MRM library (https://metlin.scripps.edu) greatly expands the chemical space accessible to targeted quantitation, providing a scalable, vendor-independent path for sensitive and specific molecular detection across research, clinical, and applied applications.

Endogenous metabolites have gained increasing interest over the past 5 years largely for their implications in diagnostic and pharmaceutical biomarker discovery. METLIN (http://metlin.scripps.edu), a freely accessible web-based data repository, has been developed to assist in a broad array of metabolite research and to facilitate metabolite identification through mass analysis. METLINincludes an annotated list of known metabolite structural information that is easily cross-correlated with its catalogue of high-resolution Fourier transform mass spectrometry (FTMS) spectra, tandem mass spectrometry (MS/MS) spectra, and LC/MS data.