ArticleAnalytical chemistry2020
Retip: Retention Time Prediction for Compound Annotation in Untargeted Metabolomics.
Article in Analytical chemistry, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 98 papers.
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Who cites it
98 citing papers in PubMed, 230 citations in OpenAlex.
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- Advances in the application of artificial intelligence in mass spectrometry-based analysis of traditional Chinese medicine: compound identification and metabolic pathway elucidation.Analytical and bioanalytical chemistry · 2026Review
- Predicting Chromatographic Retention Times from Quantum-Chemical Solvation Free Energies: A Pilot Study of Oxysterols.The journal of physical chemistry. A · 2026Article
- Tracking the Metabolites of Health and Disease Using Artificial Intelligence.Diseases (Basel, Switzerland) · 2026Review
- Liver and Skeletal Muscle Metabolome Characterization in Peripartal Dairy Cows Fed Rumen-Protected Methionine or Rumen-Protected Choline.Animals : an open access journal from MDPI · 2026Article
- A Practical Roadmap for Clinical Translation of Metabolic Biomarkers: A Review.International journal of molecular sciences · 2026Review
- AI redefine untargeted metabolomics: estimating chemical amounts for a Human Exposome Project.Frontiers in public health · 2026Review
- LC-MS/MS-Based Untargeted Hydrophilic Metabolomics Using MS-DIAL.Methods in molecular biology (Clifton, N.J.) · 2026Article
- A comprehensive database for high-throughput identification of archaeal lipids using high-resolution mass spectrometry.Nature communications · 2025Article
38 more citing papers are in PubMed but not listed here.
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
Abstract
Unidentified peaks remain a major problem in untargeted metabolomics by LC-MS/MS. Confidence in peak annotations increases by combining MS/MS matching and retention time. We here show how retention times can be predicted from molecular structures. Two large, publicly available data sets were used for model training in machine learning: the Fiehn hydrophilic interaction liquid chromatography data set (HILIC) of 981 primary metabolites and biogenic amines,and the RIKEN plant specialized metabolome annotation (PlaSMA) database of 852 secondary metabolites that uses reversed-phase liquid chromatography (RPLC). Five different machine learning algorithms have been integrated into the Retip R package: the random forest, Bayesian-regularized neural network, XGBoost, light gradient-boosting machine (LightGBM), and Keras algorithms for building the retention time prediction models. A complete workflow for retention time prediction was developed in R. It can be freely downloaded from the GitHub repository (https://www.retip.app). Keras outperformed other machine learning algorithms in the test set with minimum overfitting, verified by small error differences between training, test, and validation sets. Keras yielded a mean absolute error of 0.78 min for HILIC and 0.57 min for RPLC. Retip is integrated into the mass spectrometry software tools MS-DIAL and MS-FINDER, allowing a complete compound annotation workflow. In a test application on mouse blood plasma samples, we found a 68% reduction in the number of candidate structures when searching all isomers in MS-FINDER compound identification software. Retention time prediction increases the identification rate in liquid chromatography and subsequently leads to an improved biological interpretation of metabolomics data.
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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.