Towards Generalizable <i>In Silico</i> Predictions of Differential Ion Mobility Using Machine Learning and Customized Fingerprint Engineering

Differential mobility spectrometry (DMS), a tool for separating chemically similar species (including isomers), is readily coupled to mass spectrometry to improve selectivity in analytical workflows. DMS dispersion curves, which describe the dynamic mobility experienced by an ion in a gaseous enviro...

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Bibliographic Details
Main Author:	Cailum M. K. Stienstra (16470055) (author)
Other Authors:	Christopher R. M. Ryan (16007811) (author), Daniel Demczuk (21033595) (author), Justine R. Bissonnette (12024936) (author), Anish Arjuna (21033598) (author), J. Larry Campbell (1461124) (author), W. Scott Hopkins (1282116) (author)
Published:	2025
Subjects:	Biochemistry Genetics Plant Biology Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified perform automated identification mordred descriptors using mean absolute error https :// github dynamic mobility experienced driving ” potential compute 1591 rdkit >, shap analysis feature addition pipeline dispersion curve prediction including isomers ), maximize model performance best performing model understand feature sets general prediction tool 1 ± 0 dms dispersion curves maximum ion transmission 2 </ sub feature sets dispersion behavior transmission windows dms ), silico </ “ self towards generalizable smiles codes significant improvement separation voltage semideterministic process readily coupled previous state n </ models available meoh ). mass spectrometry machine learning improve selectivity gaseous environment g .</ even automate compensation voltage cheminformatics tasks cdfs ). cations recorded art work analytical workflows accessible tool 2 v 100 000
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Towards Generalizable <i>In Silico</i> Predictions of Differential Ion Mobility Using Machine Learning and Customized Fingerprint Engineering

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