MSIonization: A Machine Learning Tool for Ionization Mode Prediction of Small Molecules.
- 1Department of Biology, University of Mississippi, Oxford, Mississippi38677, United States.
- 2Department of BioMolecular Sciences, University of Mississippi, Oxford, Mississippi38677, United States.
Abstract
Selection of the optimal ionization mode to use-positive or negative-is a critical step in liquid chromatography-mass spectrometry (LC-MS) analysis of small molecules acquired using electrospray ionization (ESI) or atmospheric pressure chemical ionization (APCI). 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. It offers a user-friendly, offline ML model with a graphical user interface (GUI) that streamlines the MS workflow and minimizes trial-and-error. MSIonization provides a practical decision-support framework that complements chemical intuition and supports experimental planning across a wide range of scientific domains. Looking ahead, it holds significant potential to integrate into AI-driven MS pipelines to predict the ionization mode of chemically diverse small molecules at a large scale to support research across several scientific domains. The package is available in the PyPI repository (https://pypi.org/project/MSIonization/) and can be installed using 'pip install MSIonization' command.
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BibTeX
@article{singh2026msionization,
title = {{MSIonization: A Machine Learning Tool for Ionization Mode Prediction of Small Molecules.}},
author = {Yatendra Singh and Bakhtyar Sepehri and Zeyad Ibrahim and Robert J Doerksen and Sixue Chen},
journal = {Analytical chemistry},
year = {2026},
month = sep,
doi = {10.1021/acs.analchem.6c01937},
url = {https://europepmc.org/article/MED/42916379},
note = {CC BY},
}
RIS
TY - JOUR
TI - MSIonization: A Machine Learning Tool for Ionization Mode Prediction of Small Molecules.
AU - Yatendra Singh
AU - Bakhtyar Sepehri
AU - Zeyad Ibrahim
AU - Robert J Doerksen
AU - Sixue Chen
PY - 2026
DA - 2026/09/01
JO - Analytical chemistry
DO - 10.1021/acs.analchem.6c01937
UR - https://europepmc.org/article/MED/42916379
AB - Selection of the optimal ionization mode to use-positive or negative-is a critical step in liquid chromatography-mass spectrometry (LC-MS) analysis of small molecules acquired using electrospray ionization (ESI) or atmospheric pressure chemical ionization (APCI). 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. It offers a user-friendly, offline ML model with a graphical user interface (GUI) that streamlines the MS workflow and minimizes trial-and-error. MSIonization provides a practical decision-support framework that complements chemical intuition and supports experimental planning across a wide range of scientific domains. Looking ahead, it holds significant potential to integrate into AI-driven MS pipelines to predict the ionization mode of chemically diverse small molecules at a large scale to support research across several scientific domains. The package is available in the PyPI repository (https://pypi.org/project/MSIonization/) and can be installed using 'pip install MSIonization' command.
SN - 0003-2700
SN - 1520-6882
ER -