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The discovery and identification of molecules in biological and environmental samples is crucial for advancing biomedical and chemical sciences.
Fast graph representation learning with PyTorch Geometric
Matthias Fey and Jan E. Lenssen · 1903
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Click chemistry: diverse chemical function from a few good reactions
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Imagenet: A large-scale hierarchical image database
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Mamas Mamas, Warwick B Dunn, Ludwig Neyses, and Royston Goodacre · 2011
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Highly accurate chemical formula prediction tool utilizing high-resolution mass spectra, ms/ms fragmentation, heuristic rules, and isotope pattern matching
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Identifying small molecules via high resolution mass spectrometry: Communicating confidence
Emma L. Schymanski, Junho Jeon, Rebekka Gulde, Kathrin Fenner, Matthias Ruff, Heinz P. Singer, and Juliane Hollender · 2014
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Ricardo R. da Silva, Pieter C. Dorrestein, and Robert A. Quinn · 2015
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Mass spectral databases for lc/ms- and gc/ms-based metabolomics: State of the field and future prospects
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Squad: 100, 000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang · 2016
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Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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Sharing and community curation of mass spectrometry data with global natural products social molecular networking
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Classyfire: automated chemical classification with a comprehensive, computable taxonomy
Yannick Djoumbou Feunang, Roman Eisner, Craig Knox, Leonid Chepelev, Janna Hastings, Gareth Owen, Eoin Fahy, Christoph Steinbeck, Shankar Subramanian, Evan Bolton, Russell Greiner, and David S. Wishart · 2016
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Fragmentation reactions using electrospray ionization mass spectrometry: an important tool for the structural elucidation and characterization of synthetic and natural products
Daniel P. Demarque, Antonio E. M. Crotti, Ricardo Vessecchi, João L. C. Lopes, and Norberto P. Lopes · 2016
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Fragmentation trees reloaded
Sebastian Böcker and Kai Dührkop · 2016
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Neural machine translation of rare words with subword units
Rico Sennrich, Barry Haddow, and Alexandra Birch · 2016
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Nontarget screening with high resolution mass spectrometry in the environment: Ready to go?
Juliane Hollender, Emma L. Schymanski, Heinz P. Singer, and P. Lee Ferguson · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Deep sets
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabás Póczos, Ruslan Salakhutdinov, and Alexander J. Smola · 2017
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Identification of small molecules using accurate mass ms/ms search
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Pubchem chemical structure standardization
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How Powerful are Graph Neural Networks?
Keyulu Xu*, Weihua Hu*, Jure Leskovec, and Stefanie Jegelka · 2018
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Mass spectrometry based approach for organic synthesis monitoring
Veronica Termopoli, Elena Torrisi, Giorgio Famiglini, Pierangela Palma, Giovanni Zappia, Achille Cappiello, Gregory W. Vandergrift, Misha Zvekic, Erik T. Krogh, and Chris G. Gill · 2019
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Sirius 4: a rapid tool for turning tandem mass spectra into metabolite structure information
Recent advances in metabolomics analysis for early drug development
Juan Carlos Alarcon-Barrera, Sarantos Kostidis, Alejandro Ondo-Mendez, and Martin Giera · 2022
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Good practices and recommendations for using and benchmarking computational metabolomics metabolite annotation tools
Niek F. de Jonge, Kevin Mildau, David Meijer, Joris J. R. Louwen, Christoph Bueschl, Florian Huber, and Justin J. J. van der Hooft · 2022
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Msnovelist: de novo structure generation from mass spectra
Michael A Stravs, Kai Dührkop, Sebastian Böcker, and Nicola Zamboni · 2022
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Tranception: Protein fitness prediction with autoregressive transformers and inference-time retrieval
Pascal Notin, Mafalda Dias, Jonathan Frazer, Javier Marchena-Hurtado, Aidan N. Gomez, Debora S. Marks, and Yarin Gal · 2022
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The critical role that spectral libraries play in capturing the metabolomics community knowledge
Wout Bittremieux, Mingxun Wang, and Pieter C Dorrestein · 2022
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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Kai Dührkop, Markus Fleischauer, Marcus Ludwig, Alexander A. Aksenov, Alexey V. Melnik, Marvin Meusel, Pieter C. Dorrestein, Juho Rousu, and Sebastian Böcker · 2019
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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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Metabolomic analysis of aspergillus niger isolated from the international space station reveals enhanced production levels of the antioxidant pyranonigrin a
Jillian Romsdahl, Adriana Blachowicz, Yi-Ming Chiang, Kasthuri Venkateswaran, and Clay CC Wang · 2020
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Mass spectrometry-based metabolomics in health and medical science: a systematic review
Xi-wu Zhang, Qiu-han Li, Zuo-di Xu, and Jin-jin Dou · 2020
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Tracking complex mixtures of chemicals in our changing environment
Beate I. Escher, Heather M. Stapleton, and Emma L. Schymanski · 2020
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Parisa Bayat, Denis Lesage, and Richard B. Cole · 2020
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Open graph benchmark: Datasets for machine learning on graphs
Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec · 2020
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http://www.casmi-contest.org/2022/index.shtml , 2022
Critical assessment of small molecule identification. casmi · 2022
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Annotating metabolite mass spectra with domain-inspired chemical formula transformers
Samuel Goldman, Jeremy Wohlwend, Martin Stražar, Guy Haroush, Ramnik J Xavier, and Connor W Coley · 2023
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Emergence of molecular structures from repository-scale self-supervised learning on tandem mass spectra
Roman Bushuiev, Anton Bushuiev, Raman Samusevich, Corinna Brungs, Josef Sivic, and Tomáš Pluskal · 2023
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Efficiently predicting high resolution mass spectra with graph neural networks
Michael Murphy, Stefanie Jegelka, Ernest Fraenkel, Tobias Kind, David Healey, and Thomas Butler · 2023
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Rapid approximate subset-based spectra prediction for electron ionization–mass spectrometry
Richard Licheng Zhu and Eric Jonas · 2023
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Proteingym: Large-scale benchmarks for protein fitness prediction and design
Pascal Notin, Aaron Kollasch, Daniel Ritter, Lood van Niekerk, Steffanie Paul, Han Spinner, Nathan J. Rollins, Ada Shaw, Rose Orenbuch, Ruben Weitzman, Jonathan Frazer, Mafalda Dias, Dinko Franceschi, Yarin Gal, and Debora S. Marks · 2023
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Small molecule machine learning: All models are wrong, some may not even be useful
Fleming Kretschmer, Jan Seipp, Marcus Ludwig, Gunnar W Klau, and Sebastian Boecker · 2023
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Buddy: molecular formula discovery via bottom-up ms/ms interrogation
Shipei Xing, Sam Shen, Banghua Xu, Xiaoxiao Li, and Tao Huan · 2023
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Reproducible ms/ms library cleaning pipeline in matchms
Niek F de Jonge, Helge Hecht, Justin JJ van der Hooft, and Florian Huber · 2023
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PubChem 2023 update
Sunghwan Kim, Jie Chen, Tiejun Cheng, Asta Gindulyte, Jia He, Siqian He, Qingliang Li, Benjamin A Shoemaker, Paul A Thiessen, Bo Yu, Leonid Zaslavsky, Jian Zhang, and Evan E Bolton · 2023
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Ms2mol: A transformer model for illuminating dark chemical space from mass spectra
T. Butler, A. Frandsen, R. Lightheart, B. Bargh, T. Kerby, K. West, and et al · 2023
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Mass2smiles: deep learning based fast prediction of structures and functional groups directly from high-resolution ms/ms spectra
David Elser, Florian Huber, and Emmanuel Gaquerel · 2023
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Pyrimidines maintain mitochondrial pyruvate oxidation to support de novo lipogenesis
Umakant Sahu, Elodie Villa, Colleen R. Reczek, Zibo Zhao, Brendan P. O’Hara, Michael D. Torno, Rohan Mishra, William D. Shannon, John M. Asara, Peng Gao, Ali Shilatifard, Navdeep S. Chandel, and Issam Ben-Sahra · 2024
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An ensemble spectral prediction (esp) model for metabolite annotation
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Small molecule metabolites: discovery of biomarkers and therapeutic targets
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