ArticleNature communications2019
The METLIN small molecule dataset for machine learning-based retention time prediction.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 81 papers.
What it found
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Who cites it
81 citing papers in PubMed.
- Times are changing but order matters: transferable prediction of small-molecule liquid chromatography retention times.Nature methods · 2026Article
- X-Align: annotation-independent cross-platform alignment of untargeted metabolomics features.Chemical science · 2026Article
- FastRet: Fast and Simple Retention Time Prediction in Liquid Chromatography.Journal of chemical information and modeling · 2026Article
- Condition-Specific LC Retention Time Prediction: Feature-Selected QSRR Versus Pretrained Graph Isomorphism Network Transfer Learning.International journal of molecular sciences · 2026Article
- Cross-domain transfer learning from peptides to metabolites using a multi-property fine-tuned LLM.Bioinformatics (Oxford, England) · 2026Article
- Unraveling Plastic-Related Chemical Exposures during Pregnancy and Their Associations with Pregnancy Outcomes Using Suspect Screening Analysis.Environment & health (Washington, D.C.) · 2026Article
- Review
- 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
- Metabolome restructuring reveals distinct virocell infection strategies in bacteriophage-host interaction.ISME communications · 2026Article
- Integrated Metabolomics and Flavor Profiling Provide Insights into the Metabolic Basis of Flavor and Nutritional Composition Differences Between Sunflower Varieties SH363 and SH361.Foods (Basel, Switzerland) · 2025Article
- ChemFM as a scaling law guided foundation model pre-trained on informative chemicals.Communications chemistry · 2025Article
- Recent Advances in Implementation of Machine Learning for Environmental Nontarget Identification.Environment & health (Washington, D.C.) · 2025Review
- A Conversational Large-Language-Model Tutor that Accelerates Machine-Learning Method Development in Routine Bioanalytical Workflows.Chembiochem : a European journal of chemical biology · 2025Article
- Do experimental projection methods outcompete retention time prediction models in non-target screening? A case study on LC/HRMS interlaboratory comparison data.The Analyst · 2025Article
- Assessing the Impact of Measurement Precision on Metabolite Identification Probability in Multidimensional Mass Spectrometry-Based, Reference-Free Metabolomics.Analytical chemistry · 2025Article
- Mass spectrometry-based metabolomic as a powerful tool to unravel the component and mechanism in TCM.Chinese medicine · 2025Review
- In Silico High-Performance Liquid Chromatography Method Development via Machine Learning.Analytical chemistry · 2025Article
- FIORA: Local neighborhood-based prediction of compound mass spectra from single fragmentation events.Nature communications · 2025Article
- Article
21 more citing papers are in PubMed but not listed here.
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Authors and funding
9 authors.
Funding
Abstract
Machine learning has been extensively applied in small molecule analysis to predict a wide range of molecular properties and processes including mass spectrometry fragmentation or chromatographic retention time. However, current approaches for retention time prediction lack sufficient accuracy due to limited available experimental data. Here we introduce the METLIN small molecule retention time (SMRT) dataset, an experimentally acquired reverse-phase chromatography retention time dataset covering up to 80,038 small molecules. To demonstrate the utility of this dataset, we deployed a deep learning model for retention time prediction applied to small molecule annotation. Results showed that in 70[Formula: see text] of the cases, the correct molecular identity was ranked among the top 3 candidates based on their predicted retention time. We anticipate that this dataset will enable the community to apply machine learning or first principles strategies to generate better models for retention time prediction.
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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.