MSIonization: A Machine Learning Tool for Ionization Mode Prediction of Small Molecules

However, determining which ionization mode provides higher ionization efficiency usually relies on empirical knowledge or trial-and-error, particularly for large and chemically diverse compound libraries, which is time-consuming and resource-intensive, representing a major barrier in MS analysis.

To address this challenge, we developed MSIonization, a machine learning (ML)-driven tool that predicts the preferred ionization mode (positive or negative) of small molecules. It performs binary classification using molecular structure (SMILES) as input to deliver rapid, scalable predictions accompanied by probability scores and applicability domain assessments, thereby facilitating selection of the preferred ionization mode and improving experimental planning.