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It is fundamental for science and technology to be able to predict chemical reactions and their properties.
Atom pairs as molecular features in structure-activity studies: definition and applications
Raymond E Carhart, Dennis H Smith, and R Venkataraghavan · 1985
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Independence of clones as a criterion for voting rules
T. N. Tideman · 1987
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Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules
David Weininger · 1988
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Smiles. 2. algorithm for generation of unique smiles notation
David Weininger, Arthur Weininger, and Joseph L Weininger · 1989
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Signature verification using a “siamese” time delay neural network
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Circular fingerprints: flexible molecular descriptors with applications from physical chemistry to adme
Robert C Glen, Andreas Bender, Catrin H Arnby, Lars Carlsson, Scott Boyer, and James Smith · 2006
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Analysis of the reactions used for the preparation of drug candidate molecules
John S Carey, David Laffan, Colin Thomson, and Mike T Williams · 2006
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Extended-connectivity fingerprints
David Rogers and Mathew Hahn · 2010
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Learning to predict chemical reactions
Matthew A Kayala, Chloé-Agathe Azencott, Jonathan H Chen, and Pierre Baldi · 2011
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The medicinal chemist’s toolbox: an analysis of reactions used in the pursuit of drug candidates
Stephen D Roughley and Allan M Jordan · 2011
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Extraction of chemical structures and reactions from the literature
Daniel Mark Lowe · 2012
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Algorithm for reaction classification
Hans Kraut, Josef Eiblmaier, Guenter Grethe, Peter low, Heinz Matuszczyk, and Heinz Saller · 2013
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Development of a novel fingerprint for chemical reactions and its application to large-scale reaction classification and similarity
Nadine Schneider, Daniel M Lowe, Roger A Sayle, and Gregory A Landrum · 2015
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Convolutional networks on graphs for learning molecular fingerprints
David Duvenaud, Dougal Maclaurin, Jorge Aguilera-Iparraguirre, Rafael Gómez-Bombarelli, Timothy Hirzel, Alán Aspuru-Guzik, and Ryan P Adams · 2015
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
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Learning deep features for discriminative localization
Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba · 2016
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Prediction of organic reaction outcomes using machine learning
Connor W Coley, Regina Barzilay, Tommi S Jaakkola, William H Green, and Klavs F Jensen · 2017
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Smiles enumeration as data augmentation for neural network modeling of molecules
Esben Jannik Bjerrum · 2017
Analyzing learned molecular representations for property prediction
Kevin Yang, Kyle Swanson, Wengong Jin, Connor Coley, Philipp Eiden, Hua Gao, Angel Guzman-Perez, Timothy Hopper, Brian Kelley, Miriam Mathea, et al · 2019
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Predicting retrosynthetic pathways using transformer-based models and a hyper-graph exploration strategy
Philippe Schwaller, Riccardo Petraglia, Valerio Zullo, Vishnu H Nair, Rico Andreas Haeuselmann, Riccardo Pisoni, Costas Bekas, Anna Iuliano, and Teodoro Laino · 2020
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Methyl anion affinities of the canonical organic functional groups
Aaron Mood, Mohammadamin Tavakoli, Eugene Gutman, Dora Kadish, Pierre Baldi, and David L Van Vranken · 2020
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Transfer learning enables the molecular transformer to predict regio-and stereoselective reactions on carbohydrates
Giorgio Pesciullesi, Philippe Schwaller, Teodoro Laino, and Jean-Louis Reymond · 2020
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Continuous representation of molecules using graph variational autoencoder
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Axiomatic attribution for deep networks
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Adam: A method for stochastic optimization, 2017
Diederik P. Kingma and Jimmy Ba · 2017
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Deep learning for chemical reaction prediction
David Fooshee, Aaron Mood, Eugene Gutman, Mohammadamin Tavakoli, Gregor Urban, Frances Liu, Nancy Huynh, David Van Vranken, and Pierre Baldi · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Molgan: An implicit generative model for small molecular graphs
Nicola De Cao and Thomas Kipf · 2018
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Graph attention networks, 2018
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
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Molecular transformer: a model for uncertainty-calibrated chemical reaction prediction
Philippe Schwaller, Teodoro Laino, Théophile Gaudin, Peter Bolgar, Christopher A Hunter, Costas Bekas, and Alpha A Lee · 2019
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Mohammadamin Tavakoli and Pierre Baldi · 2020
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A compact review of molecular property prediction with graph neural networks
Oliver Wieder, Stefan Kohlbacher, Mélaine Kuenemann, Arthur Garon, Pierre Ducrot, Thomas Seidel, and Thierry Langer · 2020
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Sherpa: Robust hyperparameter optimization for machine learning
Lars Hertel, Julian Collado, Peter Sadowski, Jordan Ott, and Pierre Baldi · 2020
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Pairwise learning to rank by neural networks revisited: Reconstruction, theoretical analysis and practical performance
Marius Köppel, Alexander Segner, Martin Wagener, Lukas Pensel, Andreas Karwath, and Stefan Kramer · 2020
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Mohammadamin Tavakoli, Aaron Mood, David Van Vranken, and Pierre Baldi · 2021
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Methyl cation affinities of canonical organic functional groups
Dora Kadish, Aaron D Mood, Mohammadamin Tavakoli, Eugene S Gutman, Pierre Baldi, and David L Van Vranken · 2021
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How attentive are graph attention networks?, 2021
Shaked Brody, Uri Alon, and Eran Yahav · 2021
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Siamese neural networks: An overview
Davide Chicco · 2021
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