Application of Large-Scale Molecular Prediction for Creating the Preferred Precursor Ions List to Enhance the Identification of Ginsenosides from the Flower Buds of .

This work was designed to evaluate the coverage of data-dependent acquisition (DDA) extensively utilized in the untargeted metabolite/component identification in the food sciences and pharmaceutical analysis. Using saponins from the flower buds of (PGF) as an example, precursor ions list (PIL)-including DDA on a Q-Orbitrap mass spectrometer could enable higher coverage than the other four MS acquisition approaches in characterizing PGF ginsenosides. A "Virtual Library of Ginsenoside" containing 13,536 ginsenoside molecules was established by -language-programmed large-scale molecular prediction, which in combination with mass defect filtering could create a new PIL involving 1859 PGF saponin precursors. We could newly obtain the MS spectra of at least 17 components and characterize 36 ginsenosides with unknown masses, among the 164 compounds identified from PGF. Conclusively, a molecular-prediction-oriented PIL in DDA can assist to discover more potentially novel molecules benefiting to the development of functional foods and new drugs.