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Identifying a small molecule from its mass spectrum is the primary open problem in computational metabolomics.
Mass spectrometric analysis. molecular rearrangements
F. W. McLafferty · 1959
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DENDRAL: A case study of the first expert system for scientific hypothesis formation
Robert K. Lindsay, Bruce G. Buchanan, Edward A. Feigenbaum, and Joshua Lederberg · 1993
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Mass spectrometry-based metabolomics
Katja Dettmer, Pavel A. Aronov, and Bruce D. Hammock · 2006
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Seven golden rules for heuristic filtering of molecular formulas obtained by accurate mass spectrometry
Tobias Kind and Oliver Fiehn · 2007
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Density functional theory and mass spectrometry of phthalate fragmentations mechanisms: Modeling hyperconjugated carbocation and radical cation complexes with neutral molecules
Yassin A. Jeilani, Beatriz H. Cardelino, and Victor M. Ibeanusi · 2011
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Current use of high-resolution mass spectrometry in the environmental sciences
F. Hernández, J. V. Sancho, M. Ibáñez, E. Abad, T. Portolés, and L. Mattioli · 2012
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Mass spectral reference libraries: An ever-expanding resource for chemical identification
Stephen Stein · 2012
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Faster mass decomposition
Kai Dührkop, Marcus Ludwig, Marvin Meusel, and Sebastian Böcker · 2013
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Illuminating the dark matter in metabolomics
Ricardo R da Silva, Pieter C Dorrestein, and Robert A Quinn · 2015
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InChI, the IUPAC international chemical identifier
Stephen R Heller, Alan McNaught, Igor Pletnev, Stephen Stein, and Dmitrii Tchekhovskoi · 2015
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
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The ChEMBL database in 2017
Anna Gaulton, Anne Hersey, Michał Nowotka, A. Patrícia Bento, Jon Chambers, David Mendez, Prudence Mutowo, Francis Atkinson, Louisa J. Bellis, Elena Cibrián-Uhalte, Mark Davies, Nathan Dedman, Anneli Karlsson, María Paula Magariños, John P. Overington, George Papadatos, Ines Smit, and Andrew R. Leach · 2016
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Attention pooling-based convolutional neural network for sentence modelling
Meng Joo Er, Yong Zhang, Ning Wang, and Mahardhika Pratama · 2016
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SPLASH, a hashed identifier for mass spectra
Gert Wohlgemuth, Sajjan S Mehta, Ramon F Mejia, Steffen Neumann, Diego Pedrosa, Tomáš Pluskal, Emma L Schymanski, Egon L Willighagen, Michael Wilson, David S Wishart, Masanori Arita, Pieter C Dorrestein, Nuno Bandeira, Mingxun Wang, Tobias Schulze, Reza M Salek, Christoph Steinbeck, Venkata Chandrasekhar Nainala, Robert Mistrik, Takaaki Nishioka, and Oliver Fiehn · 2016
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pDeep: Predicting MS/MS spectra of peptides with deep learning
Xie-Xuan Zhou, Wen-Feng Zeng, Hao Chi, Chunjie Luo, Chao Liu, Jianfeng Zhan, Si-Min He, and Zhifei Zhang · 2017
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl · 2017
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Critical assessment of small molecule identification 2016: automated methods
Emma L. Schymanski, Christoph Ruttkies, Martin Krauss, Céline Brouard, Tobias Kind, Kai Dührkop, Felicity Allen, Arpana Vaniya, Dries Verdegem, Sebastian Böcker, Juho Rousu, Huibin Shen, Hiroshi Tsugawa, Tanvir Sajed, Oliver Fiehn, Bart Ghesquière, and Steffen Neumann · 2017
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MoleculeNet: a benchmark for molecular machine learning
Zhenqin Wu, Bharath Ramsundar, Evan N. Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S. Pappu, Karl Leswing, and Vijay Pande · 2018
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METLIN: A technology platform for identifying knowns and unknowns
Carlos Guijas, J. Rafael Montenegro-Burke, Xavier Domingo-Almenara, Amelia Palermo, Benedikt Warth, Gerrit Hermann, Gunda Koellensperger, Tao Huan, Winnie Uritboonthai, Aries E. Aisporna, Dennis W. Wolan, Mary E. Spilker, H. Paul Benton, and Gary Siuzdak · 2018
Strategies for pre-training graph neural networks
Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, and Jure Leskovec · 2020
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Nist-20, 2020
National Institute of Standards and Technology · 2020
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Natural products in drug discovery: advances and opportunities
Atanas G Atanasov, Sergey B Zotchev, Verena M Dirsch, and Claudiu T Supuran · 2021
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A map of mass spectrometry-based in-silico fragmentation prediction and compound identification in metabolomics
Christoph A Krettler and Gerhard G Thallinger · 2021
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Massformer: Tandem mass spectrum prediction with graph transformers, 2021
Adamo Young, Bo Wang, and Hannes Röst · 2021
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CFM-ID 4.0: More accurate ESI-MS/MS spectral prediction and compound identification
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Cross-ring fragmentation patterns in the tandem mass spectra of underivatized sialylated oligosaccharides and their special suitability for spectrum library searching
Maria Lorna A. De Leoz, Yamil Simón-Manso, Robert J. Woods, and Stephen E. Stein · 2018
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Rapid prediction of electron–ionization mass spectrometry using neural networks
Jennifer N. Wei, David Belanger, Ryan P. Adams, and D. Sculley · 2019
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Improving MetFrag with statistical learning of fragment annotations
Christoph Ruttkies, Steffen Neumann, and Stefan Posch · 2019
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Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning
Siegfried Gessulat, Tobias Schmidt, Daniel Paul Zolg, Patroklos Samaras, Karsten Schnatbaum, Johannes Zerweck, Tobias Knaute, Julia Rechenberger, Bernard Delanghe, Andreas Huhmer, Ulf Reimer, Hans-Christian Ehrlich, Stephan Aiche, Bernhard Kuster, and Mathias Wilhelm · 2019
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
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Fast graph representation learning with PyTorch Geometric
Matthias Fey and Jan E. Lenssen · 2019
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Predicting human health from biofluid-based metabolomics using machine learning
Ethan D. Evans, Claire Duvallet, Nathaniel D. Chu, Michael K. Oberst, Michael A. Murphy, Isaac Rockafellow, David Sontag, and Eric J. Alm · 2020
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Fei Wang, Jaanus Liigand, Siyang Tian, David Arndt, Russell Greiner, and David S. Wishart · 2021
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Moldiscovery: learning mass spectrometry fragmentation of small molecules
Liu Cao, Mustafa Guler, Azat Tagirdzhanov, Yi-Yuan Lee, Alexey Gurevich, and Hosein Mohimani · 2021
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From QCEIMS to QCxMS: A tool to routinely calculate CID mass spectra using molecular dynamics
Jeroen Koopman and Stefan Grimme · 2021
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Dgl-lifesci: An open-source toolkit for deep learning on graphs in life science
Mufei Li, Jinjing Zhou, Jiajing Hu, Wenxuan Fan, Yangkang Zhang, Yaxin Gu, and George Karypis · 2021
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Graphnorm: A principled approach to accelerating graph neural network training
Tianle Cai, Shengjie Luo, Keyulu Xu, Di He, Tie-Yan Liu, and Liwei Wang · 2021
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Critical Assessment of Small Molecule Identification 2022
Oliver Fiehn · 2022
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Sign and basis invariant networks for spectral graph representation learning, 2022
Derek Lim, Joshua Robinson, Lingxiao Zhao, Tess Smidt, Suvrit Sra, Haggai Maron, and Stefanie Jegelka · 2022
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Deep kernel learning improves molecular fingerprint prediction from tandem mass spectra
Kai Dührkop · 2022
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Comparison of cosine, modified cosine, and neutral loss based spectrum alignment for discovery of structurally related molecules
Wout Bittremieux, Robin Schmid, Florian Huber, Justin J. J. van der Hooft, Mingxun Wang, and Pieter C. Dorrestein · 2022
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