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Retrosynthesis prediction is a fundamental problem in organic synthesis, where the task is to identify precursor molecules that can be used to synthesize a target molecule.
Computer-assisted design of complex organic syntheses
E. Corey and W. T. Wipke · 1969
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SMILES, a Chemical Language and Information System
D. Weininger · 1988
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A Learning Algorithm for Continually Running Fully Recurrent Neural Networks
R. J. Williams and D. Zipser · 1989
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The Logic of Chemical Synthesis: Multistep Synthesis of Complex Carbogenic Molecules (Nobel Lecture)
E. J. Corey · 1991
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Route Designer: A Retrosynthetic Analysis Tool Utilizing Automated Retrosynthetic Rule Generation
J. Law, Z. Zsoldos, A. Simon, D. Reid, Y. Liu, S. Y. Khew, A. P. Johnson, S. Major, R. A. Wade, and H. Y. Ando · 2009
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A Collection of Robust Organic Synthesis Reactions for In Silico Molecule Design
M. Hartenfeller, M. Eberle, P. Meier, C. Nieto-Oberhuber, K.-H. Altmann, G. Schneider, E. Jacoby, and S. Renner · 2011
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RDKit: Open-Source Cheminformatics Software
G. Landrum · 2016
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What’s What: The (Nearly) Definitive Guide to Reaction Role Assignment
N. Schneider, N. Stiefl, and G. A. Landrum · 2016
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Computer-assisted synthetic planning: The end of the beginning
S. Szymkuć, E. P. Gajewska, T. Klucznik, K. Molga, P. Dittwald, M. Startek, M. Bajczyk, and B. A. Grzybowski · 2016
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Neural Message Passing for Quantum Chemistry
J. Gilmer, S. S. Schoenholz, P. F. Riley, O. Vinyals, and G. E. Dahl · 2017
Cited alongside, same era.
Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network
W. Jin, C. Coley, R. Barzilay, and T. Jaakkola · 2017
Cited alongside, same era.
Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models
B. Liu, B. Ramsundar, P. Kawthekar, J. Shi, J. Gomes, Q. Luu Nguyen, S. Ho, J. Sloane, P. Wender, and V. Pande · 2017
Cited alongside, same era.
Neural-Symbolic Machine Learning for Retrosynthesis and Reaction Prediction
M. H. Segler and M. P. Waller · 2017
Cited alongside, same era.
Attention is All You Need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin · 2017
Cited alongside, same era.
Junction Tree Variational Autoencoder for Molecular Graph Generation
W. Jin, R. Barzilay, and T. Jaakkola · 2018
A graph-convolutional neural network model for the prediction of chemical reactivity
C. W. Coley, W. Jin, L. Rogers, T. F. Jamison, T. S. Jaakkola, W. H. Green, R. Barzilay, and K. F. Jensen · 2019
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Retrosynthesis Prediction with Conditional Graph Logic Network
H. Dai, C. Li, C. Coley, B. Dai, and L. Song · 2019
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Pytorch: An imperative style, high-performance deep learning library
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala · 2019
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Predicting Retrosynthetic Reactions using Self-Corrected Transformer Neural Networks
S. Zheng, J. Rao, Z. Zhang, J. Xu, and Y. Yang · 2019
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Composing Molecules with Multiple Property Constraints
W. Jin, R. Barzilay, and T. Jaakkola · 2020
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Cited alongside, same era.
A Generative Model For Electron Paths
J. Bradshaw, M. J. Kusner, B. Paige, M. H. S. Segler, and J. M. Hernández-Lobato · 2019
Cited alongside, same era.
Learning to Make Generalizable and Diverse Predictions for Retrosynthesis
B. Chen, T. Shen, T. S. Jaakkola, and R. Barzilay · 2019
Cited alongside, same era.
Prediction of Organic Reaction Outcomes Using Machine Learning
C. W. Coley, R. Barzilay, T. S. Jaakkola, W. H. Green, and K. F. Jensen
Cited in the paper.
Computer-Assisted Retrosynthesis Based on Molecular Similarity
C. W. Coley, L. Rogers, W. H. Green, and K. F. Jensen
Cited in the paper.
A graph to graphs framework for retrosynthesis prediction, 2020
C. Shi, M. Xu, H. Guo, M. Zhang, and J. Tang · 2020
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Retroxpert: Decompose retrosynthesis prediction like a chemist
C. Yan, Q. Ding, P. Zhao, S. Zheng, J. YANG, Y. Yu, and J. Huang · 2020
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Energy-based view of retrosynthesis, 2021
R. Sun, H. Dai, L. Li, S. Kearnes, and B. Dai · 2021
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