Predicting the Predicted: A Comparison of Machine Learning-Based Collision Cross-Section Prediction Models for Small Molecules
Year: 2024
Type: article
Abstract: The application of machine learning (ML) to -omics research is growing at an exponential rate owing to the increasing availability of large amounts of data for model training. Specifically, in metabolomics, ML has enabled the prediction of tandem mass spectrometry and retention time data. More recently, due to the advent of ion mobility, new ML models have been introduced for collision cross-section (CCS) prediction, but those have been trained with different and relatively small data sets covering a few thousands of small molecules, which hampers their systematic comparison. Here, we compared four existing ML-based CCS prediction models and their capacity to predict CCS values using the recently introduced METLIN-CCS data set. We also compared them with simple linear models and with ML models that used fingerprints as regressors. We analyzed the role of structural diversity of the data on which the ML models are trained with and explored the practical application of these models for metabolite annotation using CCS values. Results showed a limited capability of the existing models to achieve the necessary accuracy to be adopted for routine metabolomics analysis. We showed that for a particular molecule, this accuracy could only be improved when models were trained with a large number of structurally similar counterparts. Therefore, we suggest that current annotation capabilities will only be significantly altered with models trained with heterogeneous data sets composed of large homogeneous hubs of structurally similar molecules to those being predicted.
Source: Analytical Chemistry
Language: English
Subfield: Spectroscopy
Field: Chemistry
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,500
Funders: “la Caixa” Foundation, European Commission, Fundación Bancaria Caixa d'Estalvis i Pensions de Barcelona, National Institutes of Health, Agencia Estatal de Investigación, National Cancer Institute, National Institute of General Medical Sciences, H2020 Industrial Leadership, Center for Biomedical Informatics and Information Technology, National Cancer Institute
Awards: G1147957589, G1175115578, G1255325603, G1474362867, Colonization resistance: training and research towards microbiome derived solutions to foodborne disease, G2262748287, G2601397997, G300979063, G3480869486, G3681454997, G4042783231, G425668148, G434337789, G4907608350, G5080475149, G5132640529, G5195188938, G5252489644, Mummichog 3, aligning mass spectrometry data to biological networks, G6004110409, G7084143925, G7685134749, G7731791180, Global Omic Data Integration on Animal, Vegetal and Environment Sectors, G8180838787, G8684520763, Metabolomics Technologies to Advance Biomedical Research
OpenAlex ID: W4398782925