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Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) experiments require a suitable match of the matrix and target compounds to achieve a selective and sensitive analysis. However, it is still difficult to predict which metabolites are ionizable with a given matrix and which factors lead to an efficient ionization. In the present study, we extracted structural properties of metabolites that contribute to their ionization in MALDI-MS analyses exploiting our experimental data set. The MALDI-MS experiment was performed for 200 standard metabolites using 9-aminoacridine (9-AA) as the matrix. We then developed a prediction model for the ionization profiles (both the ionizability and ionization efficiency) of metabolites using a quantitative structure-property relationship (QSPR) approach. The classification model for the ionizability achieved a 91% accuracy, and the regression model for the ionization efficiency reached a rank correlation coefficient of 0.77. An analysis of the descriptors contributing to such model construction suggested that the proton affinity is a major determinant of the ionization, whereas some substructures hinder efficient ionization. This study will lead to the development of more rational and predictable MALDI-MS analyses.

journal of the american society for mass spectrometry

MALDI Efficiency of Metabolites Quantitatively Associated with their Structural Properties: A Quantitative Structure–Property Relationship (QSPR) Approach