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

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% 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.

Authors:

^[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

Publication Date:: Fri Dec 20 00:00:00 EST 2019

Research Org.:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

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

OSTI Identifier:: 1624228

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

Resource Type:: Accepted Manuscript

Journal Name:: Nature Communications

Additional Journal Information:: Journal Volume: 10; Journal Issue: 1; Journal ID: ISSN 2041-1723

Publisher:: Nature Publishing Group

Country of Publication:: United States

Language:: English

Subject:: 59 BASIC BIOLOGICAL SCIENCES; Science & Technology - Other Topics

Citation Formats


                    Domingo-Almenara, Xavier, Guijas, Carlos, Billings, Elizabeth, Montenegro-Burke, J. Rafael, Uritboonthai, Winnie, Aisporna, Aries E., Chen, Emily, Benton, H. Paul, and Siuzdak, Gary. The METLIN small molecule dataset for machine learning-based retention time prediction.  United States: N. p., 2019. 
Web.  doi:10.1038/s41467-019-13680-7.

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                    Domingo-Almenara, Xavier, Guijas, Carlos, Billings, Elizabeth, Montenegro-Burke, J. Rafael, Uritboonthai, Winnie, Aisporna, Aries E., Chen, Emily, Benton, H. Paul, & Siuzdak, Gary. The METLIN small molecule dataset for machine learning-based retention time prediction.  United States.  https://doi.org/10.1038/s41467-019-13680-7

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                    Domingo-Almenara, Xavier, Guijas, Carlos, Billings, Elizabeth, Montenegro-Burke, J. Rafael, Uritboonthai, Winnie, Aisporna, Aries E., Chen, Emily, Benton, H. Paul, and Siuzdak, Gary. Fri .  
"The METLIN small molecule dataset for machine learning-based retention time prediction".  United States.  https://doi.org/10.1038/s41467-019-13680-7.  https://www.osti.gov/servlets/purl/1624228.

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@article{osti_1624228,

  title        = {The METLIN small molecule dataset for machine learning-based retention time prediction},

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

  abstractNote = {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.},

  doi          = {10.1038/s41467-019-13680-7},

  journal      = {Nature Communications},

  number       = 1,

  volume       = 10,

  place        = {United States},

  year         = {Fri Dec 20 00:00:00 EST 2019},

  month        = {Fri Dec 20 00:00:00 EST 2019}

}

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Figures / Tables:

Fig. 1: RT prediction results. a Composition of the SMRT dataset and structure of the deep-learning model. b Predicted vs experimental RT for the training set and c validation set. Non-retained molecules are indicated (tentatively) in the training set plot. The relative prediction error box plot for the validation setmore »

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Works referenced in this record:

CSM-lig: a web server for assessing and comparing protein–small molecule affinities
journal, May 2016

Pires, Douglas E. V.; Ascher, David B.
Nucleic Acids Research, Vol. 44, Issue W1
DOI: 10.1093/nar/gkw390

UPLC–MS retention time prediction: a machine learning approach to metabolite identification in untargeted profiling
journal, November 2015

Wolfer, Arnaud M.; Lozano, Sylvain; Umbdenstock, Thierry
Metabolomics, Vol. 12, Issue 1
DOI: 10.1007/s11306-015-0888-2

Mind the Gap: Mapping Mass Spectral Databases in Genome-Scale Metabolic Networks Reveals Poorly Covered Areas
journal, September 2018

Frainay, Clément; Schymanski, Emma; Neumann, Steffen
Metabolites, Vol. 8, Issue 3
DOI: 10.3390/metabo8030051

MoleculeNet: a benchmark for molecular machine learning
journal, January 2018

Wu, Zhenqin; Ramsundar, Bharath; Feinberg, Evan N.
Chemical Science, Vol. 9, Issue 2
DOI: 10.1039/C7SC02664A

A study on retention “projection” as a supplementary means for compound identification by liquid chromatography–mass spectrometry capable of predicting retention with different gradients, flow rates, and instruments
journal, September 2011

Boswell, Paul G.; Schellenberg, Jonathan R.; Carr, Peter W.
Journal of Chromatography A, Vol. 1218, Issue 38
DOI: 10.1016/j.chroma.2011.07.105

Use of dual-filtering to create training sets leading to improved accuracy in quantitative structure-retention relationships modelling for hydrophilic interaction liquid chromatographic systems
journal, July 2017

Taraji, Maryam; Haddad, Paul R.; Amos, Ruth I. J.
Journal of Chromatography A, Vol. 1507
DOI: 10.1016/j.chroma.2017.05.044

Beyond the hype: deep neural networks outperform established methods using a ChEMBL bioactivity benchmark set
journal, August 2017

Lenselink, Eelke B.; ten Dijke, Niels; Bongers, Brandon
Journal of Cheminformatics, Vol. 9, Issue 1
DOI: 10.1186/s13321-017-0232-0

Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking
journal, August 2016

Wang, Mingxun; Carver, Jeremy J.; Phelan, Vanessa V.
Nature Biotechnology, Vol. 34, Issue 8
DOI: 10.1038/nbt.3597

Development and application of retention time prediction models in the suspect and non-target screening of emerging contaminants
journal, February 2019

Aalizadeh, Reza; Nika, Maria-Christina; Thomaidis, Nikolaos S.
Journal of Hazardous Materials, Vol. 363
DOI: 10.1016/j.jhazmat.2018.09.047

Extended-Connectivity Fingerprints
journal, April 2010

Rogers, David; Hahn, Mathew
Journal of Chemical Information and Modeling, Vol. 50, Issue 5
DOI: 10.1021/ci100050t

Review on modelling aspects in reversed-phase liquid chromatographic quantitative structure–retention relationships
journal, October 2007

Put, R.; Vander Heyden, Y.
Analytica Chimica Acta, Vol. 602, Issue 2
DOI: 10.1016/j.aca.2007.09.014

CFM-ID: a web server for annotation, spectrum prediction and metabolite identification from tandem mass spectra
journal, June 2014

Allen, Felicity; Pon, Allison; Wilson, Michael
Nucleic Acids Research, Vol. 42, Issue W1
DOI: 10.1093/nar/gku436

Toward Global Metabolomics Analysis with Hydrophilic Interaction Liquid Chromatography–Mass Spectrometry: Improved Metabolite Identification by Retention Time Prediction
journal, November 2011

Creek, Darren J.; Jankevics, Andris; Breitling, Rainer
Analytical Chemistry, Vol. 83, Issue 22
DOI: 10.1021/ac2021823

CFM-ID 3.0: Significantly Improved ESI-MS/MS Prediction and Compound Identification
journal, April 2019

Djoumbou-Feunang, Yannick; Pon, Allison; Karu, Naama
Metabolites, Vol. 9, Issue 4
DOI: 10.3390/metabo9040072

SIRIUS 4: a rapid tool for turning tandem mass spectra into metabolite structure information
journal, March 2019

Dührkop, Kai; Fleischauer, Markus; Ludwig, Marcus
Nature Methods, Vol. 16, Issue 4
DOI: 10.1038/s41592-019-0344-8

Mass spectral databases for LC/MS- and GC/MS-based metabolomics: State of the field and future prospects
journal, April 2016

Vinaixa, Maria; Schymanski, Emma L.; Neumann, Steffen
TrAC Trends in Analytical Chemistry, Vol. 78
DOI: 10.1016/j.trac.2015.09.005

Retention time prediction for dereplication of natural products (CxHyOz) in LC–MS metabolite profiling
journal, December 2014

Eugster, Philippe J.; Boccard, Julien; Debrus, Benjamin
Phytochemistry, Vol. 108
DOI: 10.1016/j.phytochem.2014.10.005

A Simple Representation of Three-Dimensional Molecular Structure
journal, August 2017

Axen, Seth D.; Huang, Xi-Ping; Cáceres, Elena L.
Journal of Medicinal Chemistry, Vol. 60, Issue 17
DOI: 10.1021/acs.jmedchem.7b00696

Molecular fingerprint similarity search in virtual screening
journal, January 2015

Cereto-Massagué, Adrià; Ojeda, María José; Valls, Cristina
Methods, Vol. 71
DOI: 10.1016/j.ymeth.2014.08.005

METLIN: A Technology Platform for Identifying Knowns and Unknowns
journal, January 2018

Guijas, Carlos; Montenegro-Burke, J. Rafael; Domingo-Almenara, Xavier
Analytical Chemistry, Vol. 90, Issue 5
DOI: 10.1021/acs.analchem.7b04424

Liquid-chromatography retention order prediction for metabolite identification
journal, September 2018

Bach, Eric; Szedmak, Sandor; Brouard, Céline
Bioinformatics, Vol. 34, Issue 17
DOI: 10.1093/bioinformatics/bty590

Autonomous METLIN-Guided In-source Fragment Annotation for Untargeted Metabolomics
journal, January 2019

Domingo-Almenara, Xavier; Montenegro-Burke, J. Rafael; Guijas, Carlos
Analytical Chemistry, Vol. 91, Issue 5
DOI: 10.1021/acs.analchem.8b03126

XCMS-MRM and METLIN-MRM: a cloud library and public resource for targeted analysis of small molecules
journal, August 2018

Domingo-Almenara, Xavier; Montenegro-Burke, J. Rafael; Ivanisevic, Julijana
Nature Methods, Vol. 15, Issue 9
DOI: 10.1038/s41592-018-0110-3

Structure Annotation of All Mass Spectra in Untargeted Metabolomics
journal, January 2019

Blaženović, Ivana; Kind, Tobias; Sa, Michael R.
Analytical Chemistry, Vol. 91, Issue 3
DOI: 10.1021/acs.analchem.8b04698

Comprehensive and Empirical Evaluation of Machine Learning Algorithms for Small Molecule LC Retention Time Prediction
journal, January 2019

Bouwmeester, Robbin; Martens, Lennart; Degroeve, Sven
Analytical Chemistry, Vol. 91, Issue 5
DOI: 10.1021/acs.analchem.8b05820

Retention Index Prediction Using Quantitative Structure–Retention Relationships for Improving Structure Identification in Nontargeted Metabolomics
journal, June 2018

Wen, Yabin; Amos, Ruth I. J.; Talebi, Mohammad
Analytical Chemistry, Vol. 90, Issue 15
DOI: 10.1021/acs.analchem.8b02084

Rapid Prediction of Electron–Ionization Mass Spectrometry Using Neural Networks
journal, March 2019

Wei, Jennifer N.; Belanger, David; Adams, Ryan P.
ACS Central Science, Vol. 5, Issue 4
DOI: 10.1021/acscentsci.9b00085

Predicting retention time in hydrophilic interaction liquid chromatography mass spectrometry and its use for peak annotation in metabolomics
journal, September 2014

Cao, Mingshu; Fraser, Karl; Huege, Jan
Metabolomics, Vol. 11, Issue 3
DOI: 10.1007/s11306-014-0727-x

Retention Time Prediction Improves Identification in Nontargeted Lipidomics Approaches
journal, July 2015

Aicheler, Fabian; Li, Jia; Hoene, Miriam
Analytical Chemistry, Vol. 87, Issue 15
DOI: 10.1021/acs.analchem.5b01139

Improved Peptide Retention Time Prediction in Liquid Chromatography through Deep Learning
journal, August 2018

Ma, Chunwei; Ren, Yan; Yang, Jiarui
Analytical Chemistry, Vol. 90, Issue 18
DOI: 10.1021/acs.analchem.8b02386

ClassyFire: automated chemical classification with a comprehensive, computable taxonomy
journal, November 2016

Djoumbou Feunang, Yannick; Eisner, Roman; Knox, Craig
Journal of Cheminformatics, Vol. 8, Issue 1
DOI: 10.1186/s13321-016-0174-y

DeepTox: Toxicity Prediction using Deep Learning
journal, February 2016

Mayr, Andreas; Klambauer, Günter; Unterthiner, Thomas
Frontiers in Environmental Science, Vol. 3
DOI: 10.3389/fenvs.2015.00080

Proposed minimum reporting standards for chemical analysis: Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI)
journal, September 2007

Sumner, Lloyd W.; Amberg, Alexander; Barrett, Dave
Metabolomics, Vol. 3, Issue 3
DOI: 10.1007/s11306-007-0082-2

HMDB 4.0: the human metabolome database for 2018
journal, November 2017

Wishart, David S.; Feunang, Yannick Djoumbou; Marcu, Ana
Nucleic Acids Research, Vol. 46, Issue D1
DOI: 10.1093/nar/gkx1089

PDB-wide collection of binding data: current status of the PDBbind database
journal, October 2014

Liu, Zhihai; Li, Yan; Han, Li
Bioinformatics, Vol. 31, Issue 3
DOI: 10.1093/bioinformatics/btu626

Software Tools and Approaches for Compound Identification of LC-MS/MS Data in Metabolomics
journal, May 2018

Blaženović, Ivana; Kind, Tobias; Ji, Jian
Metabolites, Vol. 8, Issue 2
DOI: 10.3390/metabo8020031

Enabling Efficient and Confident Annotation of LC−MS Metabolomics Data through MS1 Spectrum and Time Prediction
journal, September 2016

Broeckling, Corey D.; Ganna, Andrea; Layer, Mark
Analytical Chemistry, Vol. 88, Issue 18
DOI: 10.1021/acs.analchem.6b02479

The use of LC predicted retention times to extend metabolites identification with SWATH data acquisition
journal, December 2017

Bruderer, Tobias; Varesio, Emmanuel; Hopfgartner, Gérard
Journal of Chromatography B, Vol. 1071
DOI: 10.1016/j.jchromb.2017.07.016

The rise of deep learning in drug discovery
journal, June 2018

Chen, Hongming; Engkvist, Ola; Wang, Yinhai
Drug Discovery Today, Vol. 23, Issue 6
DOI: 10.1016/j.drudis.2018.01.039

PredRet: Prediction of Retention Time by Direct Mapping between Multiple Chromatographic Systems
journal, August 2015

Stanstrup, Jan; Neumann, Steffen; Vrhovšek, Urška
Analytical Chemistry, Vol. 87, Issue 18
DOI: 10.1021/acs.analchem.5b02287

Easy and accurate high-performance liquid chromatography retention prediction with different gradients, flow rates, and instruments by back-calculation of gradient and flow rate profiles
journal, September 2011

Boswell, Paul G.; Schellenberg, Jonathan R.; Carr, Peter W.
Journal of Chromatography A, Vol. 1218, Issue 38
DOI: 10.1016/j.chroma.2011.07.070

Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations?
journal, May 2015

Bajusz, Dávid; Rácz, Anita; Héberger, Károly
Journal of Cheminformatics, Vol. 7, Issue 1
DOI: 10.1186/s13321-015-0069-3

Kernel-Based, Partial Least Squares Quantitative Structure-Retention Relationship Model for UPLC Retention Time Prediction: A Useful Tool for Metabolite Identification
journal, September 2016

Falchi, Federico; Bertozzi, Sine Mandrup; Ottonello, Giuliana
Analytical Chemistry, Vol. 88, Issue 19
DOI: 10.1021/acs.analchem.6b02075

Quantitative structure–retention relationships models for prediction of high performance liquid chromatography retention time of small molecules: Endogenous metabolites and banned compounds
journal, October 2013

Goryński, Krzysztof; Bojko, Barbara; Nowaczyk, Alicja
Analytica Chimica Acta, Vol. 797
DOI: 10.1016/j.aca.2013.08.025

Retention projection enables accurate calculation of liquid chromatographic retention times across labs and methods
journal, September 2015

Abate-Pella, Daniel; Freund, Dana M.; Ma, Yan
Journal of Chromatography A, Vol. 1412
DOI: 10.1016/j.chroma.2015.07.108

Machine learning in chemoinformatics and drug discovery
journal, August 2018

Lo, Yu-Chen; Rensi, Stefano E.; Torng, Wen
Drug Discovery Today, Vol. 23, Issue 8
DOI: 10.1016/j.drudis.2018.05.010

Annotation: A Computational Solution for Streamlining Metabolomics Analysis
journal, November 2017

Domingo-Almenara, Xavier; Montenegro-Burke, J. Rafael; Benton, H. Paul
Analytical Chemistry, Vol. 90, Issue 1
DOI: 10.1021/acs.analchem.7b03929

Evaluation of an Artificial Neural Network Retention Index Model for Chemical Structure Identification in Nontargeted Metabolomics
journal, October 2018

Samaraweera, Milinda A.; Hall, L. Mark; Hill, Dennis W.
Analytical Chemistry, Vol. 90, Issue 21
DOI: 10.1021/acs.analchem.8b03118

DeepTox: Toxicity prediction using deep learning
journal, October 2017

Klambauer, Günter; Unterthiner, Thomas; Mayr, Andreas
Toxicology Letters, Vol. 280
DOI: 10.1016/j.toxlet.2017.07.175

Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking
text, January 2016

M., Waters, Katrina; Yao, Peng,; L., McPhail, Kerry
The University of North Carolina at Chapel Hill University Libraries
DOI: 10.17615/dd92-mf79

Beyond the Hype: Deep Neural Networks Outperform Established Methods Using A ChEMBL Bioactivity Benchmark Set
dataset, January 2019

Lenselink, Eelke Bart; ten Dijke, N. (Niels); Bongers, Brandon
4TU.Centre for Research Data
DOI: 10.4121/uuid:b64986dd-3203-445e-9b93-13a5ac7ef999

A Simple Representation of Three-Dimensional Molecular Structure
text, January 2017

M. J., Keiser,; S. D., Axen,; B. L., Roth,
American Chemical Society
DOI: 10.17615/paa0-1p40

MoleculeNet: A Benchmark for Molecular Machine Learning
preprint, January 2017

Wu, Zhenqin; Ramsundar, Bharath; Feinberg, Evan N.
arXiv
DOI: 10.48550/arxiv.1703.00564

The role of machine learning method in the synthesis and biological ınvestigation of heterocyclic compounds
journal, October 2021

Mermer, Arif
Molecular Diversity, Vol. 26, Issue 3
DOI: 10.1007/s11030-021-10264-w

Predicting retention time in hydrophilic interaction liquid chromatography mass spectrometry and its use for peak annotation in metabolomics
journal, September 2014

Cao, Mingshu; Fraser, Karl; Huege, Jan
Metabolomics, Vol. 11, Issue 3
DOI: 10.1007/s11306-014-0727-x

Review on modelling aspects in reversed-phase liquid chromatographic quantitative structure–retention relationships
journal, October 2007

Put, R.; Vander Heyden, Y.
Analytica Chimica Acta, Vol. 602, Issue 2
DOI: 10.1016/j.aca.2007.09.014

Quantitative structure–retention relationships models for prediction of high performance liquid chromatography retention time of small molecules: Endogenous metabolites and banned compounds
journal, October 2013

Goryński, Krzysztof; Bojko, Barbara; Nowaczyk, Alicja
Analytica Chimica Acta, Vol. 797
DOI: 10.1016/j.aca.2013.08.025

Easy and accurate high-performance liquid chromatography retention prediction with different gradients, flow rates, and instruments by back-calculation of gradient and flow rate profiles
journal, September 2011

Boswell, Paul G.; Schellenberg, Jonathan R.; Carr, Peter W.
Journal of Chromatography A, Vol. 1218, Issue 38
DOI: 10.1016/j.chroma.2011.07.070

A study on retention “projection” as a supplementary means for compound identification by liquid chromatography–mass spectrometry capable of predicting retention with different gradients, flow rates, and instruments
journal, September 2011

Boswell, Paul G.; Schellenberg, Jonathan R.; Carr, Peter W.
Journal of Chromatography A, Vol. 1218, Issue 38
DOI: 10.1016/j.chroma.2011.07.105

Retention projection enables accurate calculation of liquid chromatographic retention times across labs and methods
journal, September 2015

Abate-Pella, Daniel; Freund, Dana M.; Ma, Yan
Journal of Chromatography A, Vol. 1412
DOI: 10.1016/j.chroma.2015.07.108

The use of LC predicted retention times to extend metabolites identification with SWATH data acquisition
journal, December 2017

Bruderer, Tobias; Varesio, Emmanuel; Hopfgartner, Gérard
Journal of Chromatography B, Vol. 1071
DOI: 10.1016/j.jchromb.2017.07.016

Development and application of retention time prediction models in the suspect and non-target screening of emerging contaminants
journal, February 2019

Aalizadeh, Reza; Nika, Maria-Christina; Thomaidis, Nikolaos S.
Journal of Hazardous Materials, Vol. 363
DOI: 10.1016/j.jhazmat.2018.09.047

Molecular fingerprint similarity search in virtual screening
journal, January 2015

Cereto-Massagué, Adrià; Ojeda, María José; Valls, Cristina
Methods, Vol. 71
DOI: 10.1016/j.ymeth.2014.08.005

Toward Global Metabolomics Analysis with Hydrophilic Interaction Liquid Chromatography–Mass Spectrometry: Improved Metabolite Identification by Retention Time Prediction
journal, November 2011

Creek, Darren J.; Jankevics, Andris; Breitling, Rainer
Analytical Chemistry, Vol. 83, Issue 22
DOI: 10.1021/ac2021823

METLIN: A Technology Platform for Identifying Knowns and Unknowns
journal, January 2018

Guijas, Carlos; Montenegro-Burke, J. Rafael; Domingo-Almenara, Xavier
Analytical Chemistry, Vol. 90, Issue 5
DOI: 10.1021/acs.analchem.7b04424

Retention Index Prediction Using Quantitative Structure–Retention Relationships for Improving Structure Identification in Nontargeted Metabolomics
journal, June 2018

Wen, Yabin; Amos, Ruth I. J.; Talebi, Mohammad
Analytical Chemistry, Vol. 90, Issue 15
DOI: 10.1021/acs.analchem.8b02084

Improved Peptide Retention Time Prediction in Liquid Chromatography through Deep Learning
journal, August 2018

Ma, Chunwei; Ren, Yan; Yang, Jiarui
Analytical Chemistry, Vol. 90, Issue 18
DOI: 10.1021/acs.analchem.8b02386

Autonomous METLIN-Guided In-source Fragment Annotation for Untargeted Metabolomics
journal, January 2019

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DOI: 10.1021/acs.analchem.8b03126

Structure Annotation of All Mass Spectra in Untargeted Metabolomics
journal, January 2019

Blaženović, Ivana; Kind, Tobias; Sa, Michael R.
Analytical Chemistry, Vol. 91, Issue 3
DOI: 10.1021/acs.analchem.8b04698

Comprehensive and Empirical Evaluation of Machine Learning Algorithms for Small Molecule LC Retention Time Prediction
journal, January 2019

Bouwmeester, Robbin; Martens, Lennart; Degroeve, Sven
Analytical Chemistry, Vol. 91, Issue 5
DOI: 10.1021/acs.analchem.8b05820

Extended-Connectivity Fingerprints
journal, April 2010

Rogers, David; Hahn, Mathew
Journal of Chemical Information and Modeling, Vol. 50, Issue 5
DOI: 10.1021/ci100050t

DIAMetAlyzer allows automated false-discovery rate-controlled analysis for data-independent acquisition in metabolomics
journal, March 2022

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Nature Communications, Vol. 13, Issue 1
DOI: 10.1038/s41467-022-29006-z

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journal, August 2018

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Nature Methods, Vol. 15, Issue 9
DOI: 10.1038/s41592-018-0110-3

Liquid-chromatography retention order prediction for metabolite identification
journal, September 2018

Bach, Eric; Szedmak, Sandor; Brouard, Céline
Bioinformatics, Vol. 34, Issue 17
DOI: 10.1093/bioinformatics/bty590

CFM-ID: a web server for annotation, spectrum prediction and metabolite identification from tandem mass spectra
journal, June 2014

Allen, Felicity; Pon, Allison; Wilson, Michael
Nucleic Acids Research, Vol. 42, Issue W1
DOI: 10.1093/nar/gku436

HMDB 4.0: the human metabolome database for 2018
journal, November 2017

Wishart, David S.; Feunang, Yannick Djoumbou; Marcu, Ana
Nucleic Acids Research, Vol. 46, Issue D1
DOI: 10.1093/nar/gkx1089

DeepTox: Toxicity Prediction using Deep Learning
journal, February 2016

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DOI: 10.3389/fenvs.2015.00080

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journal, May 2018

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Metabolites, Vol. 8, Issue 2
DOI: 10.3390/metabo8020031

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journal, September 2018

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DOI: 10.3390/metabo8030051

MoleculeNet: A Benchmark for Molecular Machine Learning
preprint, January 2017

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arXiv
DOI: 10.48550/arxiv.1703.00564