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Georg Wittig and Werner Haag · 1955
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A new stereospecific cross-coupling by the palladium-catalyzed reaction of 1-alkenylboranes with 1-alkenyl or 1-alkynyl halides
Norio Miyaura, Kinji Yamada, and Akira Suzuki · 1979
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The first practical method for asymmetric epoxidation
Tsutomu Katsuki and K Barry Sharpless · 1980
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A conservation law for generalization performance
Cullen Schaffer · 1994
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IUPAC. Compendium of Chemical Terminology, 2nd ed. (the "Gold Book")
A. D. Mcnaught and A. Wilkinson · 1997
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No free lunch theorems for optimization
David H Wolpert and William G Macready · 1997
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The supervised learning no-free-lunch theorems
David H Wolpert · 2002
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Diclofenac solubility: independent determination of the intrinsic solubility of three crystal forms
Antonio Llinas, Jonathan C Burley, Karl J Box, Robert C Glen, and Jonathan M Goodman · 2007
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A machine learning approach to predict chemical reactions
Matthew A Kayala and Pierre F Baldi · 2011
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Extraction of chemical structures and reactions from the literature
Daniel Mark Lowe · 2012
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Predicting organic reaction outcomes with weisfeiler-lehman network
Wengong Jin, Connor Coley, Regina Barzilay, and Tommi Jaakkola · 2017
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Retrosynthetic reaction prediction using neural sequence-to-sequence models
Bowen Liu, Bharath Ramsundar, Prasad Kawthekar, Jade Shi, Joseph Gomes, Quang Luu Nguyen, Stephen Ho, Jack Sloane, Paul Wender, and Vijay Pande · 2017
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Computer-assisted retrosynthesis based on molecular similarity
Connor W Coley, Luke Rogers, William H Green, and Klavs F Jensen · 2017
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What uncertainties do we need in bayesian deep learning for computer vision?
Alex Kendall and Yarin Gal · 2017
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Grammar variational autoencoder
Matt J Kusner, Brooks Paige, and José Miguel Hernández-Lobato · 2017
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Constrained bayesian optimization for automatic chemical design
Ryan-Rhys Griffiths and José Miguel Hernández-Lobato · 2017
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Chemts: an efficient python library for de novo molecular generation
Xiufeng Yang, Jinzhe Zhang, Kazuki Yoshizoe, Kei Terayama, and Koji Tsuda · 2017
Cited alongside, same era.
Reaction Prediction and Synthesis Design , chapter 4.2, pages 86–105
Jonathan M. Goodman · 2018
Cited alongside, same era.
Molgan: An implicit generative model for small molecular graphs
Nicola De Cao and Thomas Kipf · 2018
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Junction tree variational autoencoder for molecular graph generation
Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2018
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Molecular hypergraph grammar with its application to molecular optimization
Hiroshi Kajino · 2018
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Optimization of molecules via deep reinforcement learning
Zhenpeng Zhou, Steven Kearnes, Li Li, Richard N Zare, and Patrick Riley · 2018
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Graph convolutional policy network for goal-directed molecular graph generation
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A Graph-Convolutional Neural Network Model for the Prediction of Chemical Reactivity
Connor W. Coley, Wengong Jin, Luke Rogers, Timothy F. Jamison, Tommi S Jaakkola, William H. Green, Regina Barzilay, and Klavs F. Jensen · 2018
Cited alongside, same era.
John Bradshaw, Matt J Kusner, Brooks Paige, Marwin HS Segler, and José Miguel Hernández-Lobato · 2018
Cited alongside, same era.
Planning chemical syntheses with deep neural networks and symbolic ai
Marwin HS Segler, Mike Preuss, and Mark P Waller · 2018
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?found in translation?: predicting outcomes of complex organic chemistry reactions using neural sequence-to-sequence models
Philippe Schwaller, Theophile Gaudin, David Lanyi, Costas Bekas, and Teodoro Laino · 2018
Cited alongside, same era.
Minnorm training: an algorithm for training overcomplete deep neural networks
Yamini Bansal, Madhu Advani, David D Cox, and Andrew M Saxe · 2018
Cited alongside, same era.
Most ligand-based classification benchmarks reward memorization rather than generalization
Izhar Wallach and Abraham Heifets · 2018
Cited alongside, same era.
Automatic chemical design using a data-driven continuous representation of molecules
Rafael Gómez-Bombarelli, Jennifer N Wei, David Duvenaud, José Miguel Hernández-Lobato, Benjamín Sánchez-Lengeling, Dennis Sheberla, Jorge Aguilera-Iparraguirre, Timothy D Hirzel, Ryan P Adams, and Alán Aspuru-Guzik · 2018
Cited alongside, same era.
Jiaxuan You, Bowen Liu, Rex Ying, Vijay Pande, and Jure Leskovec · 2018
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Deep reinforcement learning for de novo drug design
Mariya Popova, Olexandr Isayev, and Alexander Tropsha · 2018
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Entangled conditional adversarial autoencoder for de novo drug discovery
Daniil Polykovskiy, Alexander Zhebrak, Dmitry Vetrov, Yan Ivanenkov, Vladimir Aladinskiy, Polina Mamoshina, Marine Bozdaganyan, Alexander Aliper, Alex Zhavoronkov, and Artur Kadurin · 2018
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Prototype-based compound discovery using deep generative models
Shahar Harel and Kira Radinsky · 2018
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Latent molecular optimization for targeted therapeutic design
Tristan Aumentado-Armstrong · 2018
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Application of generative autoencoder in de novo molecular design
Thomas Blaschke, Marcus Olivecrona, Ola Engkvist, Jürgen Bajorath, and Hongming Chen · 2018
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Exploring deep recurrent models with reinforcement learning for molecule design
Daniel Neil, Marwin Segler, Laura Guasch, Mohamed Ahmed, Dean Plumbley, Matthew Sellwood, and Nathan Brown · 2018
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Graph transformation policy network for chemical reaction prediction
Anonymous · 2019
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