High-Throughput Measurement and Machine Learning-Based Prediction of Collision Cross Sections for Drugs and Drug Metabolites

Drug
metabolite identification is a bottleneck of drug metabolism
studies due to the need for time-consuming chromatographic separation
and structural confirmation. Ion mobility-mass spectrometry (IM-MS),
on the other hand, separates analytes on a rapid (millisecond) time
scale and enables the measurement of collision cross section (CCS),
a unique physical property related to an ion’s gas-phase size
and shape, which can be used as an additional parameter for identification
of unknowns. A current limitation to the application of IM-MS to the
identification of drug metabolites is the lack of reference CCS values.
In this work, we assembled a large-scale database of drug and drug
metabolite CCS values using high-throughput in vitro drug metabolite
generation and a rapid IM-MS analysis with automated data processing.
Subsequently, we used this database to train a machine learning-based
CCS prediction model, employing a combination of conventional 2D molecular
descriptors and novel 3D descriptors, achieving high prediction accuracies
(0.8–2.2% median relative error on test set data). The inclusion
of 3D information in the prediction model enables the prediction of
different CCS values for different protomers, conformers, and positional
isomers, which is not possible using conventional 2D descriptors.
The prediction models, dmCCS, are available at https://CCSbase.net/dmccs_predictions.