ArticleJournal of the American Society for Mass Spectrometry2022
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 18 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed.
- Integrating Computational Chemistry and Ion Mobility Spectrometry into Non-Targeted Analysis Workflows for the Structural Elucidation of Emerging PFAS.Journal of the American Society for Mass Spectrometry · 2026Article
- How to Use Quantum Chemistry for Analyzing Mass Spectrometry Data.Mass spectrometry (Tokyo, Japan) · 2026Review
- PEAS: An Application for Autonomous Precision Conformation Sampling.Journal of chemical information and modeling · 2025Article
- Assigning Peptide Structure from Ion-Mobility Mass Spectrometry Collision Cross Section Data.Journal of the American Society for Mass Spectrometry · 2025Article
- State Ensemble Energy Recognition (SEER): A Hybrid Gas-Phase Molecular Charge State Predictor.Journal of chemical information and modeling · 2025Article
- Integrating Machine Learning and Quantum Circuits for Proton Affinity Predictions.Journal of chemical theory and computation · 2025Article
- In Silico Characterization of Glycan Ions from IM-MS Collision Cross Section.Journal of the American Society for Mass Spectrometry · 2025Article
- Eliminating the Deadwood: A Machine Learning Model for CCS Knowledge-Based Conformational Focusing for Lipids.Journal of chemical information and modeling · 2024Article
- Evaluating the generalizability of graph neural networks for predicting collision cross section.Journal of cheminformatics · 2024Article
- Spatially and temporally probing distinctive glycerophospholipid alterations in Alzheimer's disease mouse brain via high-resolution ion mobility-enabled sn-position resolved lipidomics.Nature communications · 2024Article
- Predicting the Predicted: A Comparison of Machine Learning-Based Collision Cross-Section Prediction Models for Small Molecules.Analytical chemistry · 2024Article
- Systematic Comparison of Experimental Crystallographic Geometries and Gas-Phase Computed Conformers for Torsion Preferences.Journal of chemical information and modeling · 2023Article
- Collision cross section measurement and prediction methods in omics.Journal of mass spectrometry : JMS · 2023Review
- MassCCS: A High-Performance Collision Cross-Section Software for Large Macromolecular Assemblies.Journal of chemical information and modeling · 2023Article
- Collision Cross Section Prediction Based on Machine Learning.Molecules (Basel, Switzerland) · 2023Review
- CCS Predictor 2.0: An Open-Source Jupyter Notebook Tool for Filtering Out False Positives in Metabolomics.Analytical chemistry · 2022Article
- Comparability of Steroid Collision Cross Sections Using Three Different IM-HRMS Technologies: An Interplatform Study.Journal of the American Society for Mass Spectrometry · 2022Article
- An anchored experimental design and meta-analysis approach to address batch effects in large-scale metabolomics.Frontiers in molecular biosciences · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
The interpretation of ion mobility coupled to mass spectrometry (IM-MS) data to predict unknown structures is challenging and depends on accurate theoretical estimates of the molecular ion collision cross section (CCS) against a buffer gas in a low or atmospheric pressure drift chamber. The sensitivity and reliability of computational prediction of CCS values depend on accurately modeling the molecular state over accessible conformations. In this work, we developed an efficient CCS computational workflow using a machine learning model in conjunction with standard DFT methods and CCS calculations. Furthermore, we have performed Traveling Wave IM-MS (TWIMS) experiments to validate the extant experimental values and assess uncertainties in experimentally measured CCS values. The developed workflow yielded accurate structural predictions and provides unique insights into the likely preferred conformation analyzed using IM-MS experiments. The complete workflow makes the computation of CCS values tractable for a large number of conformationally flexible metabolites with complex molecular structures.
Indexed as
Identifiers
What OpenQuestion holds
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.