2026-10-11T19:46:23Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33863746 2026-09-16T17:10:37Z category_1 category_4 category_19 category_873 category_734 category_69 category_133 portal_63 item_type_3 month_year_09_2026

MSIonization: A Machine Learning Tool for Ionization Mode Prediction of Small Molecules Yatendra Singh (6456179) Bakhtyar Sepehri (3136362) Zeyad Ibrahim (22754438) Robert J. Doerksen (1328514) Sixue Chen (121659) Biophysics Biochemistry Pharmacology Chemical Sciences not elsewhere classified Biological Sciences not elsewhere classified Inorganic Chemistry Plant Biology thereby facilitating selection scalable predictions accompanied offline ml model improving experimental planning holds significant potential complements chemical intuition applicability domain assessments preferred ionization mode optimal ionization mode ionization mode prediction https :// pypi small molecules selection pip install msionization machine learning tool graphical user interface driven ms pipelines use – positive ionization mode small molecules machine learning pypi repository wide range support framework scientific domains probability scores practical decision negative – msionization provides msionization /) ms workflow major barrier looking ahead installed using empirical knowledge developed msionization deliver rapid critical step apci ). 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. 2026-09-16T00:00:00Z Dataset Dataset 10.1021/acs.analchem.6c01937.s002 https://figshare.com/articles/dataset/MSIonization_A_Machine_Learning_Tool_for_Ionization_Mode_Prediction_of_Small_Molecules/33863746 CC BY-NC 4.0