ArticleJournal of the American Society for Mass Spectrometry2022
High-Throughput Measurement and Machine Learning-Based Prediction of Collision Cross Sections for Drugs and Drug Metabolites.
Article in Journal of the American Society for Mass Spectrometry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Candidate Structure Prioritization in Non-Target Screening with Predicted Collision Cross-Sections and Uncertainty Quantification.Journal of chemical information and modeling · 2026Article
- ToxBase: A Multidimensional ToxCast Reference Database for High-Throughput Human Exposome Analysis.Environmental science & technology · 2026Article
- A resource to empirically establish drug exposure records directly from untargeted metabolomics data.Nature communications · 2025Article
- Assessing the Impact of Measurement Precision on Metabolite Identification Probability in Multidimensional Mass Spectrometry-Based, Reference-Free Metabolomics.Analytical chemistry · 2025Article
- Predicting the Predicted: A Comparison of Machine Learning-Based Collision Cross-Section Prediction Models for Small Molecules.Analytical chemistry · 2024Article
- Computational tools and algorithms for ion mobility spectrometry-mass spectrometry.Proteomics · 2024Review
- Interfacing High-Throughput Electrosynthesis and Mass Spectrometric Analysis of Azines.Analytical chemistry · 2024Article
- Measurement of very low-molecular weight metabolites by traveling wave ion mobility and its use in human urine samples.Journal of pharmaceutical analysis · 2024Article
- Development and Application of a Multidimensional Database for the Detection of Quaternary Ammonium Compounds and Their Phase I Hepatic Metabolites in Humans.Environmental science & technology · 2024Article
- HILIC-IM-MS for Simultaneous Lipid and Metabolite Profiling of Bacteria.ACS measurement science au · 2024Article
- Potential of measured relative shifts in collision cross section values for biotransformation studies.Analytical and bioanalytical chemistry · 2024Article
- Collision cross section measurement and prediction methods in omics.Journal of mass spectrometry : JMS · 2023Review
- Recent advances in data- and knowledge-driven approaches to explore primary microbial metabolism.Current opinion in chemical biology · 2023Review
- Collision Cross Section Prediction Based on Machine Learning.Molecules (Basel, Switzerland) · 2023Review
- Ion Mobility Mass Spectrometry (IM-MS) for Structural Biology: Insights Gained by Measuring Mass, Charge, and Collision Cross Section.Chemical reviews · 2023Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.