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

Structure Annotation of All Mass Spectra in Untargeted Metabolomics

(2019) Ivana Blaženović et al.   ANALYTICAL CHEMISTRY

Rapid Prediction of Electron–Ionization Mass Spectrometry Using Neural Networks

(2019) Jennifer N. Wei et al.   ACS Central Science

SIRIUS 4: a rapid tool for turning tandem mass spectra into metabolite structure information

(2019) Kai Dührkop et al.   NATURE METHODS

Comprehensive and Empirical Evaluation of Machine Learning Algorithms for Small Molecule LC Retention Time Prediction

(2019) Robbin Bouwmeester et al.   ANALYTICAL CHEMISTRY

Autonomous METLIN-Guided In-source Fragment Annotation for Untargeted Metabolomics

(2019) Xavier Domingo-Almenara et al.   ANALYTICAL CHEMISTRY

CFM-ID 3.0: Significantly Improved ESI-MS/MS Prediction and Compound Identification

(2019) Yannick Djoumbou-Feunang et al.   Metabolites

Retention Index Prediction Using Quantitative Structure–Retention Relationships for Improving Structure Identification in Nontargeted Metabolomics

(2018) Yabin Wen et al.   ANALYTICAL CHEMISTRY

METLIN: A Technology Platform for Identifying Knowns and Unknowns

(2018) Carlos Guijas et al.   ANALYTICAL CHEMISTRY

OUP accepted manuscript

(2018)   BIOINFORMATICS

Machine learning in chemoinformatics and drug discovery

(2018) Yu-Chen Lo et al.   DRUG DISCOVERY TODAY

The rise of deep learning in drug discovery

(2018) Hongming Chen et al.   DRUG DISCOVERY TODAY

MoleculeNet: a benchmark for molecular machine learning

(2018) Zhenqin Wu et al.   Chemical Science

Improved Peptide Retention Time Prediction in Liquid Chromatography through Deep Learning

(2018) Chunwei Ma et al.   ANALYTICAL CHEMISTRY

Development and Application of Retention Time Prediction Models in the Suspect and Non-target Screening of Emerging Contaminants

(2018) Reza Aalizadeh et al.   JOURNAL OF HAZARDOUS MATERIALS

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

(2018) Xavier Domingo-Almenara et al.   NATURE METHODS

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

(2018) Milinda A. Samaraweera et al.   ANALYTICAL CHEMISTRY

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

(2017) Maryam Taraji et al.   JOURNAL OF CHROMATOGRAPHY A

The use of LC predicted retention times to extend metabolites identification with SWATH data acquisition

(2017) Tobias Bruderer et al.   JOURNAL OF CHROMATOGRAPHY B-ANALYTICAL TECHNOLOGIES IN THE BIOMEDICAL AND LIFE SCIENCES

A Simple Representation of Three-Dimensional Molecular Structure

(2017) Seth D. Axen et al.   JOURNAL OF MEDICINAL CHEMISTRY

HMDB 4.0: the human metabolome database for 2018

(2017) David S Wishart et al.   NUCLEIC ACIDS RESEARCH

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

(2017) Eelke B. Lenselink et al.   Journal of Cheminformatics

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

(2016) Corey D. Broeckling et al.   ANALYTICAL CHEMISTRY

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

(2016) Federico Falchi et al.   ANALYTICAL CHEMISTRY

Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking

(2016) Mingxun Wang et al.   NATURE BIOTECHNOLOGY

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

(2016) Douglas E.V. Pires et al.   NUCLEIC ACIDS RESEARCH

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

(2016) Maria Vinaixa et al.   TRAC-TRENDS IN ANALYTICAL CHEMISTRY

ClassyFire: automated chemical classification with a comprehensive, computable taxonomy

(2016) Yannick Djoumbou Feunang et al.   Journal of Cheminformatics

PredRet: Prediction of Retention Time by Direct Mapping between Multiple Chromatographic Systems

(2015) Jan Stanstrup et al.   ANALYTICAL CHEMISTRY

Retention Time Prediction Improves Identification in Nontargeted Lipidomics Approaches

(2015) Fabian Aicheler et al.   ANALYTICAL CHEMISTRY

Retention projection enables accurate calculation of liquid chromatographic retention times across labs and methods

(2015) Daniel Abate-Pella et al.   JOURNAL OF CHROMATOGRAPHY A

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

(2015) Arnaud M. Wolfer et al.   Metabolomics

Molecular fingerprint similarity search in virtual screening

(2015) Adrià Cereto-Massagué et al.   METHODS

Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations?

(2015) Dávid Bajusz et al.   Journal of Cheminformatics

PDB-wide collection of binding data: current status of the PDBbind database

(2014) Zhihai Liu et al.   BIOINFORMATICS

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

(2014) Mingshu Cao et al.   Metabolomics

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

(2014) Felicity Allen et al.   NUCLEIC ACIDS RESEARCH

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

(2014) Philippe J. Eugster et al.   PHYTOCHEMISTRY

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

(2013) Krzysztof Goryński et al.   ANALYTICA CHIMICA ACTA

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

(2011) Darren J. Creek et al.   ANALYTICAL CHEMISTRY

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

(2011) Paul G. Boswell et al.   JOURNAL OF CHROMATOGRAPHY A

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

(2011) Paul G. Boswell et al.   JOURNAL OF CHROMATOGRAPHY A

Extended-Connectivity Fingerprints

(2010) David Rogers et al.   Journal of Chemical Information and Modeling