Quantum Chemistry Calculation‐Assisted Large‐Scale Collision Cross Section Prediction Empowers Derivatization‐Enhanced Multidimensional Metabolomics

Abstract Derivatization‐enhanced multidimensional metabolomics combined with ion mobility mass spectrometry will greatly improve the accuracy and coverage of metabolic analysis. However, accurate prediction of the large‐scale collision cross section (CCS) of derivatized metabolites without relying on standards and the establishment of multidimensional analytical methods faces great challenges. Here, we propose quantum chemistry calculation‐assisted machine learning strategies applicable to the accurate prediction of the CCS of derivatized sterols, develop C═C bond‐targeted N‐Me derivatization methods for unsaturated sterols, and create a large‐scale, 4D information database of derivatized sterol lipids ( n = 4891) by combining retention time and fragment ion prediction. Furthermore, a high‐coverage unsaturated sterolomics at the isomer level was established on this basis, which quantitatively revealed the tissue‐specific distribution patterns of over 100 sterol lipids. This study provides a key foundation for derivatization‐enhanced metabolomics and provides important techniques and information for metabolic and functional studies of sterols.