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We propose a new model for making generalizable and diverse retrosynthetic reaction predictions.
Computer-assisted design of complex organic syntheses
E. J. Corey and W. Todd Wipke · 1969
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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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Building and refining a knowledge base for synthetic organic chemistry via the methodology of inductive and deductive machine learning
Herbert Gelernter, J Royce Rose, and Chyouhwa Chen · 1990
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The logic of chemical synthesis: multistep synthesis of complex carbogenic molecules (nobel lecture)
Elias James Corey · 1991
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A novel approach to retrosynthetic analysis using knowledge bases derived from reaction databases
Koji Satoh and Kimito Funatsu · 1999
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Extraction of chemical structures and reactions from the literature
Daniel Mark Lowe · 2012
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Diverse beam search: Decoding diverse solutions from neural sequence models
Ashwin K Vijayakumar, Michael Cogswell, Ramprasath R Selvaraju, Qing Sun, Stefan Lee, David Crandall, and Dhruv Batra · 2016
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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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Predicting organic reaction outcomes with weisfeiler-lehman network
Wengong Jin, Connor W. Coley, Regina Barzilay, and Tommi S. Jaakkola · 2017
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OpenNMT: Open-source toolkit for neural machine translation
Guillaume Klein, Yoon Kim, Yuntian Deng, Jean Senellart, and Alexander M. Rush · 2017
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Grammar variational autoencoder
Matt J Kusner, Brooks Paige, and José Miguel Hernández-Lobato · 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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Neural-symbolic machine learning for retrosynthesis and reaction prediction
Marwin H. S. Segler and Mark P. Waller · 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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Machine learning in computer-aided synthesis planning
Connor W. Coley, William H. Green, and Klavs F. Jensen · 2018
Sequence to sequence mixture model for diverse machine translation
Xuanli He, Gholamreza Haffari, and Mohammad Norouzi · 2018
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Learning deep generative models of graphs
Yujia Li, Oriol Vinyals, Chris Dyer, Razvan Pascanu, and Peter W. Battaglia · 2018
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Enhancing retrosynthetic reaction prediction with deep learning using multiscale reaction classification
Javier L Baylon, Nicholas A Cilfone, Jeffrey R Gulcher, and Thomas W Chittenden · 2019
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Pre-training graph neural networks
Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, and Jure Leskovec · 2019
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A transformer model for retrosynthesis
Pavel Karpov, Guillaume Godin, and Igor V Tetko · 2019
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Automatic chemical design using a data-driven continuous representation of molecules
Rafael Gómez-Bombarelli, Jennifer N. Wei, David Duvenaud, JoséMiguel Hernández-Lobato, Benjamín Sánchez-Lengeling, Dennis Sheberla, Jorge Aguilera-Iparraguirre, Timothy D. Hirzel, Ryan P. Adams, and Alán Aspuru-Guzik · 2018
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Junction tree variational autoencoder for molecular graph generation
Wengong Jin, Regina Barzilay, and Tommi Jaakkola
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Learning multimodal graph-to-graph translation for molecular optimization
Wengong Jin, Kevin Yang, Regina Barzilay, and Tommi S. Jaakkola
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Automatic retrosynthetic pathway planning using template-free models
Kangjie Lin, Youjun Xu, Jianfeng Pei, and Luhua Lai · 2019
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Molecular Transformer – A Model for Uncertainty-Calibrated Chemical Reaction Prediction
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