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Towards Generalizable In Silico Predictions of Differential Ion Mobility Using Machine Learning and Customized

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Area of Science:

  • Analytical Chemistry
  • Computational Chemistry
  • Machine Learning

Background:

  • Differential mobility spectrometry (DMS) coupled with mass spectrometry enhances selectivity for separating chemical species, including isomers.
  • DMS dispersion curves, reflecting ion mobility, are crucial for optimizing analyte transmission but lack fast, general prediction tools.
  • Predicting ion dispersion behavior is essential for advancing analytical workflows and instrument automation.

Purpose of the Study:

  • To develop a machine learning (ML) model for generalized prediction of ion dispersion curves in DMS.
  • To create an in silico feature addition pipeline for generating molecular descriptors from SMILES codes.
  • To improve the accuracy and applicability of DMS analysis, especially in solvent-modified environments.

Main Methods:

  • Utilized a dataset of 1141 dispersion curve measurements for anions and cations in N2 and N2/methanol environments.
  • Developed an in silico feature addition pipeline to compute 1591 RDKit and Mordred descriptors from SMILES codes.
  • Employed cumulative density functions (CDFs) for normalizing molecular descriptors and applied ML models, including explainability techniques (SHAP).

Main Results:

  • Achieved a mean absolute error (MAE) of 2.1 ± 0.2 V for dispersion curve prediction, outperforming previous methods.
  • The feature addition pipeline demonstrated a semideterministic process for feature set generation.
  • The model successfully predicted dispersion curves in solvent-modified environments, a novel capability.

Conclusions:

  • The developed ML model provides a fast, general prediction tool for DMS dispersion curves.
  • This approach accelerates and potentially automates prescreening in complex analytical workflows like 2D LC×DMS.
  • The tool enhances the 'self-driving' potential of DMS instruments by automating identification of transmission windows.