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Retrosynthesis prediction focuses on identifying reactants capable of synthesizing a target product.
Chemical reactions from US patents (1976-Sep2016)
Lowe, D. 2017 · 1976
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Deep generative models for 3D linker design
Imrie, F.; Bradley, A. R.; van der Schaar, M.; and Deane, C. M. 2020 · 1995
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Denoising diffusion probabilistic models
Ho, J.; Jain, A.; and Abbeel, P. 2020 · 2006
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Energy-based view of retrosynthesis
Sun, R.; Dai, H.; Li, L.; Kearnes, S.; and Dai, B. 2020 · 2007
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Open Babel: An open chemical toolbox
O’Boyle, N. M.; Banck, M.; James, C. A.; Morley, C.; Vandermeersch, T.; and Hutchison, G. R. 2011 · 2011
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Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J.; Weiss, E. A.; Maheswaranathan, N.; and Ganguli, S. 2015 · 2015
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Retrosynthetic reaction prediction using neural sequence-to-sequence models
Liu, B.; Ramsundar, B.; Kawthekar, P.; Shi, J.; Gomes, J.; Luu Nguyen, Q.; Ho, S.; Sloane, J.; Wender, P.; and Pande, V. 2017 · 2017
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Modeling relational data with graph convolutional networks
Schlichtkrull, M.; Kipf, T. N.; Bloem, P.; van den Berg, R.; Titov, I.; and Welling, M. 2017 · 2017
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Attention is all you need
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, Ł.; and Polosukhin, I. 2017 · 2017
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Graph convolutional policy network for goal-directed molecular graph generation
You, J.; ; Liu, B.; Ying, R.; Pande, V.; and Leskovec, J. 2018 · 2018
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Retrosynthesis prediction with conditional graph logic network
Dai, H.; Li, C.; Coley, C.; Dai, B.; and Song, L. 2019 · 2019
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Deep graph infomax
Velickovic, P.; Fedus, W.; Hamilton, W. L.; Liò, P.; Bengio, Y.; and Hjelm, R. D. 2019 · 2019
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A graph to graphs framework for retrosynthesis prediction
Shi, C.; Xu, M.; Guo, H.; Zhang, M.; and Tang, J. 2020 · 2020
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State-of-the-art augmented NLP transformer models for direct and single-step retrosynthesis
Tetko, I. V.; Karpov, P.; Van Deursen, R.; and Godin, G. 2020 · 2020
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Retroxpert: Decompose retrosynthesis prediction like a chemist
Yan, C.; Ding, Q.; Zhao, P.; Zheng, S.; Yang, J.; Yu, Y.; and Huang, J. 2020 · 2020
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Predicting retrosynthetic reactions using self-corrected transformer neural networks
Zheng, S.; Zheng, S.; Rao, J.; Zhang, Z.; Xu, J.; and Yang, Y. 2020 · 2020
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Deep retrosynthetic reaction prediction using local reactivity and global attention
Chen, S.; and Jung, Y. 2021 · 2021
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Molecule edit graph attention network: modeling chemical reactions as sequences of graph edits
Sacha, M.; Błaz, M.; Byrski, P.; Dabrowski-Tumanski, P.; Chrominski, M.; Loska, R.; Włodarczyk-Pruszynski, P.; and Jastrzebski, S. 2021 · 2021
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E(n) equivariant normalizing flows
Satorras, V. G.; Hogeboom, E.; Fuchs, F.; Posner, I.; and Welling, M. 2021 · 2021
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GTA: Graph truncated attention for retrosynthesis
Permutation invariant graph-to-sequence model for template-free retrosynthesis and reaction prediction
Tu, Z.; and Coley, C. W. 2022 · 2022
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Retroformer: Pushing the limits of end-to-end retrosynthesis transformer
Wan, Y.; Hsieh, C.-Y.; Liao, B.; and Zhang, S. 2022 · 2022
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Geodiff: A geometric diffusion model for molecular conformation generation
Xu, M.; Yu, L.; Song, Y.; Shi, C.; Ermon, S.; and Tang, J. 2022 · 2022
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RetroComposer: Composing templates for template-based retrosynthesis prediction
Yan, C.; Zhao, P.; Lu, C.; Yu, Y.; and Huang, J. 2022 · 2022
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Torchdrug: A powerful and flexible machine learning platform for drug discovery
Zhu, Z.; Shi, C.; Zhang, Z.; Liu, S.; Xu, M.; Yuan, X.; Zhang, Y.; Chen, J.; Cai, H.; Lu, J.; et al. 2022 · 2022
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Seo, S.-W.; Song, Y. Y.; Yang, J. Y.; Bae, S.; Lee, H.; Shin, J.; Hwang, S. J.; and Yang, E. 2021 · 2021
Cited alongside, same era.
Learning gradient fields for molecular conformation generation
Shi, C.; Luo, S.; Xu, M.; and Tang, J. 2021 · 2021
Cited alongside, same era.
Learning graph models for retrosynthesis prediction
Somnath, V. R.; Bunne, C.; Coley, C.; Krause, A.; and Barzilay, R. 2021 · 2021
Cited alongside, same era.
Score-based generative modeling through stochastic differential equations
Song, Y.; Sohl-Dickstein, J.; Kingma, D. P.; Kumar, A.; Ermon, S.; and Poole, B. 2021 · 2021
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RetroPrime: A diverse, plausible and Transformer-based method for single-step retrosynthesis predictions
Wang, X.; Li, Y.; Qiu, J.; Chen, G.; Liu, H.; Liao, B.; Hsieh, C.-Y.; and Yao, X. 2021 · 2021
Cited alongside, same era.
Equivariant diffusion for molecule generation in 3D
Hoogeboom, E.; Satorras, V. G.; Vignac, C.; and Welling, M. 2022 · 2022
Cited alongside, same era.
Equivariant 3D-conditional diffusion models for molecular linker design
Igashov, I.; Stärk, H.; Vignac, C.; Satorras, V. G.; Frossard, P.; Welling, M.; Bronstein, M.; and Correia, B. 2022 · 2022
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Later among the works it cites.
G2Retro as a two-step graph generative models for retrosynthesis prediction
Chen, Z.; Ayinde, O. R.; Fuchs, J. R.; Sun, H.; and Ning, X. 2023 · 2023
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Large Language Models as Topological Structure Enhancers for Text-Attributed Graphs
Sun, S.; Ren, Y.; Ma, C.; and Zhang, X. 2023 · 2023
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RetroDiff: Retrosynthesis as multi-stage distribution interpolation
Wang, Y.; Song, Y.; Xu, M.; Wang, R.; Zhou, H.; and Ma, W.-Y. 2023 · 2023
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Adding conditional control to text-to-image diffusion models
Zhang, L.; Rao, A.; and Agrawala, M. 2023 · 2023
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A latent diffusion model for protein structure generation
Cong, F.; Yan, K.; Wang, L.; Au, W. Y.; McThrow, M. C.; Komikado, T.; Maruhashi, K.; Uchino, K.; Qian, X.; and Ji, S. 2024 · 2024
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Diffusion models in bioinformatics and computational biology
Guo, Z.; Liu, J.; Wang, Y.; Chen, M.; Wang, D.; Xu, D.; and Cheng, J. 2024 · 2024
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Retrobridge: Modeling retrosynthesis with Markov bridges
Igashov, I.; Schneuing, A.; Segler, M.; Bronstein, M.; and Correia1, B. 2024 · 2024
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Antibody design using a score-based diffusion model guided by evolutionary, physical and geometric constraints
Tian, Z.; Ren, M.; and Zhang, H. 2024 · 2024
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