Predicting the Predicted: A Comparison of Machine Learning-Based Collision Cross-Section Prediction Models for Small Molecules-Reference-Cited by-同舟云学术

Predicting the Predicted: A Comparison of Machine Learning-Based Collision Cross-Section Prediction Models for Small Molecules

Published:2024-05-24 Issue:22 Volume:96 Page:9088-9096
ISSN:0003-2700
Container-title:Analytical Chemistry
language:en
Short-container-title:Anal. Chem.

Author:

de Cripan Sara M.¹²³^ORCID,Arora Trisha¹²³,Olomí Adrià¹²,Canela Núria²^ORCID,Siuzdak Gary⁴^ORCID,Domingo-Almenara Xavier¹²³^ORCID

Affiliation:

1. Computational Metabolomics for Systems Biology Lab, Eurecat─Technology Centre of Catalonia, Barcelona 08005, Catalonia, Spain

2. Centre for Omics Sciences (COS), Unique Scientific and Technical Infrastructures (ICTS), Eurecat─Technology Centre of Catalonia & Rovira i Virgili University Joint Unit, Reus 43204, Catalonia, Spain

3. Department of Electrical, Electronic and Control Engineering (DEEEA), Universitat Rovira i Virgili, Tarragona 43007, Catalonia, Spain

4. Scripps Center of Metabolomics and Mass Spectrometry, Department of Chemistry, Molecular and Computational Biology, Scripps Research Institute, La Jolla, California 92037, United States

Funder

Center for Biomedical Informatics and Information Technology

'la Caixa' Foundation

H2020 Industrial Leadership

National Institute of General Medical Sciences

Agencia Estatal de Investigaci?n,Ministerio de Ciencia, Innovaci?n y Universidades

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.analchem.4c00630

Reference24 articles.

1. Importance of collision cross section measurements by ion mobility mass spectrometry in structural biology

2. Collision cross section measurement and prediction methods in omics

3. Guidelines and considerations for building multidimensional libraries for untargeted MS-based metabolomics

4. Application of Predicted Collisional Cross Section to Metabolome Databases to Probabilistically Describe the Current and Future Ion Mobility Mass Spectrometry

5. The METLIN small molecule dataset for machine learning-based retention time prediction