Chiralify: A Framework for Fragmentation Modeling and Advanced Chiral Metabolite Annotation

Abstract Metabolomics enables the comprehensive profiling of small molecules in biological systems and has become essential for investigating disease-associated metabolic alterations and biomarker discovery. Advances in high-resolution mass spectrometry and automated computational workflows have greatly expanded the scale and throughput of metabolite profiling. However, conventional untargeted metabolomics pipelines generally lack the capability to distinguish enantiomeric metabolites based on mass spectrometry data alone. This limitation is critical, as many metabolites exist as mirror-image forms, namely enantiomers, which are detected in human tissues and biofluids and may play distinct roles in disease-related metabolic alterations. Despite advances in chiral separation strategies using chiral selectors or derivatization reagents, the analysis of enantiomeric metabolites remains largely manual, limiting high-throughput analysis and biomarker discovery. Here we introduce Chiralify, an open-source cheminformatics framework designed to enable automated and scalable untargeted chiral metabolomics. Chiralify integrates fragmentation modeling and an expandable spectral library for metabolite annotation with a flexible framework for multiplexed analysis of chiral metabolomics datasets. The platform automates the detection, annotation, and comparative analysis of enantiomeric metabolites, providing a standardized and scalable solution for incorporating stereochemical information into metabolomics workflows. Scientific Contribution Chiralify is an open-source cheminformatics framework that automates and streamlines chiral metabolomics analysis, enabling systematic characterization of enantiomeric metabolic heterogeneity. Application to diverse cancer cell lines revealed numerous enantiomeric metabolite pairs and previously unexplored patterns of stereochemical diversity. Chiralify provides a standardized and scalable computational approach that extends cheminformatics to stereochemically resolved metabolomics and establishes stereochemistry as an additional dimension for metabolite annotation and biomarker discovery.