ArticleCommunications chemistry2023
Highly accurate and large-scale collision cross sections prediction with graph neural networks.
Article in Communications chemistry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- A Novel Workflow for Fast Elucidation of Drug Metabolites for Screening-Combining In Silico Metabolite Prediction With Trapped Ion Mobility QTOF-MS.Drug testing and analysis · 2026Article
- Separation of sulfated fucose-containing isomers found in polysaccharides from brown seaweed using cyclic ion mobility mass spectrometry.RSC advances · 2026Article
- Review
- Review
- Metabolomic and lipidomic atlas of human hair across its length.Communications biology · 2025Article
- DistRMI: a deep distance-aware neural network for explainable RNA loop motif-small molecule interaction prediction.Briefings in bioinformatics · 2025Article
- Practical Guidance on Selecting Analytical Methods for PFAS in Semiconductor Manufacturing Wastewater.ACS measurement science au · 2025Review
- Assessing the Impact of Measurement Precision on Metabolite Identification Probability in Multidimensional Mass Spectrometry-Based, Reference-Free Metabolomics.Analytical chemistry · 2025Article
- Review
- Coupling Capillary Electrophoresis With a Shifted Inlet Potential High-Resolution Ion Mobility Spectrometer.Electrophoresis · 2025Article
- IMPACT-4CCS: Integrated Modeling and Prediction Using Ab Initio and Trained Potentials for Collision Cross Sections.Journal of computational chemistry · 2025Article
- Ion Mobility-Coupled Mass Spectrometry for Metallophore Detection.Journal of natural products · 2025Article
- PubChemLite Plus Collision Cross Section (CCS) Values for Enhanced Interpretation of Nontarget Environmental Data.Environmental science & technology letters · 2025Article
- Evaluating the generalizability of graph neural networks for predicting collision cross section.Journal of cheminformatics · 2024Article
- Computational tools and algorithms for ion mobility spectrometry-mass spectrometry.Proteomics · 2024Review
- Deep-Learning-Based Mixture Identification for Nuclear Magnetic Resonance Spectroscopy Applied to Plant Flavors.Molecules (Basel, Switzerland) · 2023Article
- Highly accurate and large-scale collision cross sections prediction with graph neural networks.Communications chemistry · 2023Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
The collision cross section (CCS) values derived from ion mobility spectrometry can be used to improve the accuracy of compound identification. Here, we have developed the Structure included graph merging with adduct method for CCS prediction (SigmaCCS) based on graph neural networks using 3D conformers as inputs. A model was trained, evaluated, and tested with >5,000 experimental CCS values. It achieved a coefficient of determination of 0.9945 and a median relative error of 1.1751% on the test set. The model-agnostic interpretation method and the visualization of the learned representations were used to investigate the chemical rationality of SigmaCCS. An in-silico database with 282 million CCS values was generated for three different adduct types of 94 million compounds. Its source code is publicly available at https://github.com/zmzhang/SigmaCCS . Altogether, SigmaCCS is an accurate, rational, and off-the-shelf method to directly predict CCS values from molecular structures.
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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.