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

Domingo-Almenara, Xavier; Guijas, Carlos; Billings, Elizabeth; Montenegro-Burke, J. Rafael; Uritboonthai, Winnie; Aisporna, Aries E.; Chen, Emily; Benton, H. Paul; Siuzdak, Gary

doi:10.1038/s41467-019-13680-7

Title: The METLIN small molecule dataset for machine learning-based retention time prediction

Journal Article · 20 December 2019 · Nature Communications

DOI: https://doi.org/10.1038/s41467-019-13680-7 · OSTI ID:1624228

^[1]; Guijas, Carlos ^[2];

^[2];

^[2]; Uritboonthai, Winnie ^[2]; Aisporna, Aries E. ^[2]; Chen, Emily ^[3];

^[2];

^[4]

The Scripps Research Inst., La Jolla, CA (United States). Scripps Center for Metabolomics
The Scripps Research Inst., La Jolla, CA (United States). Scripps Center for Metabolomics
The Scripps Research Inst., La Jolla, CA (United States). California Institute for Biomedical Research (Calibr)
The Scripps Research Inst., La Jolla, CA (United States). Scripps Center for Metabolomics The Scripps Research Inst., La Jolla, CA (United States). Department of Integrative Structural and Computational Biology

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% 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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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: AC02-05CH11231; R35GM130385; P30 MH062261; P01 DA026146; U01 CA235493

OSTI ID:: 1624228

Journal Information:: Nature Communications, Vol. 10, Issue 1; ISSN 2041-1723

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 91 works

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Molecular fingerprint similarity search in virtual screening Cereto-Massagué, Adrià; Ojeda, María José; Valls, Cristina Methods, Vol. 71 https://doi.org/10.1016/j.ymeth.2014.08.005	journal	January 2015
Toward Global Metabolomics Analysis with Hydrophilic Interaction Liquid Chromatography–Mass Spectrometry: Improved Metabolite Identification by Retention Time Prediction Creek, Darren J.; Jankevics, Andris; Breitling, Rainer Analytical Chemistry, Vol. 83, Issue 22 https://doi.org/10.1021/ac2021823	journal	November 2011
METLIN: A Technology Platform for Identifying Knowns and Unknowns Guijas, Carlos; Montenegro-Burke, J. Rafael; Domingo-Almenara, Xavier Analytical Chemistry, Vol. 90, Issue 5 https://doi.org/10.1021/acs.analchem.7b04424	journal	January 2018
Retention Index Prediction Using Quantitative Structure–Retention Relationships for Improving Structure Identification in Nontargeted Metabolomics Wen, Yabin; Amos, Ruth I. J.; Talebi, Mohammad Analytical Chemistry, Vol. 90, Issue 15 https://doi.org/10.1021/acs.analchem.8b02084	journal	June 2018
Improved Peptide Retention Time Prediction in Liquid Chromatography through Deep Learning Ma, Chunwei; Ren, Yan; Yang, Jiarui Analytical Chemistry, Vol. 90, Issue 18 https://doi.org/10.1021/acs.analchem.8b02386	journal	August 2018
Autonomous METLIN-Guided In-source Fragment Annotation for Untargeted Metabolomics Domingo-Almenara, Xavier; Montenegro-Burke, J. Rafael; Guijas, Carlos Analytical Chemistry, Vol. 91, Issue 5 https://doi.org/10.1021/acs.analchem.8b03126	journal	January 2019
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Comprehensive and Empirical Evaluation of Machine Learning Algorithms for Small Molecule LC Retention Time Prediction Bouwmeester, Robbin; Martens, Lennart; Degroeve, Sven Analytical Chemistry, Vol. 91, Issue 5 https://doi.org/10.1021/acs.analchem.8b05820	journal	January 2019
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XCMS-MRM and METLIN-MRM: a cloud library and public resource for targeted analysis of small molecules Domingo-Almenara, Xavier; Montenegro-Burke, J. Rafael; Ivanisevic, Julijana Nature Methods, Vol. 15, Issue 9 https://doi.org/10.1038/s41592-018-0110-3	journal	August 2018
Liquid-chromatography retention order prediction for metabolite identification Bach, Eric; Szedmak, Sandor; Brouard, Céline Bioinformatics, Vol. 34, Issue 17 https://doi.org/10.1093/bioinformatics/bty590	journal	September 2018
CFM-ID: a web server for annotation, spectrum prediction and metabolite identification from tandem mass spectra Allen, Felicity; Pon, Allison; Wilson, Michael Nucleic Acids Research, Vol. 42, Issue W1 https://doi.org/10.1093/nar/gku436	journal	June 2014
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Software Tools and Approaches for Compound Identification of LC-MS/MS Data in Metabolomics Blaženović, Ivana; Kind, Tobias; Ji, Jian Metabolites, Vol. 8, Issue 2 https://doi.org/10.3390/metabo8020031	journal	May 2018
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