Classifying Host–Guest Topology with Ion Mobility-Mass Spectrometry and Machine Learning

Abstract: Elucidating the topology of host–guest complexes is essential for the rational design of supramolecular assemblies. Building on the recent success of data-driven approaches, we evaluate the combination of ion mobility–mass spectrometry (IMS–MS), density functional theory (DFT) featurization, and machine learning to predict and classify the binding modes of 1:1 complexes formed between cucurbit[6]uril (CB6) and diamine guests. Training a regression model with DFT-derived molecular descriptors and experimentally determined collisional cross sections (CCS) enables predicting the CCS of host–guest complexes with a diverse set of diamine guests. The predicted values naturally separate in two distinct groups corresponding respectively to inclusion and exclusion complexes, thereby enabling topology classification. This approach demonstrates that DFT-featurization and IMS–MS data capture well host–guest topology and provide a framework for the data-driven design of supramolecular assemblies.

Recommended citation: Quentin Duez*, Charlotte Lefebvre, Jérôme Cornil, Julien De Winter, Pascal Gerbaux. (2025). "Classifying Host–Guest Topology with Ion Mobility-Mass Spectrometry and Machine Learning." Journal of Physical Chemistry Letters. 16(30), 7551-7559.
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