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Retrosynthesis prediction is one of the fundamental challenges in organic synthesis.
The Logic of Chemical Synthesis
Corey, E., and Cheng, X · 1989
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What’s what: The (nearly) definitive guide to reaction role assignment
Schneider, N., Stiefl, N., and Landrum, G. A · 2016
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Computer-assisted retrosynthesis based on molecular similarity
Coley, C. W., Rogers, L., Green, W. H., and Jensen, K. F · 2017
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Neural message passing for quantum chemistry
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., and Dahl, G. E · 2017
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Adam: A method for stochastic optimization, 2017
Kingma, D. P., and Ba, J · 2017
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OpenNMT: Open-source toolkit for neural machine translation
Klein, G., Kim, Y., Deng, Y., Senellart, J., and Rush, A · 2017
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Regularizing neural networks by penalizing confident output distributions
Pereyra, G., Tucker, G., Chorowski, J., Kaiser, L., and Hinton, G. E · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L. u., and Polosukhin, I · 2017
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Organic synthesis provides opportunities to transform drug discovery
Blakemore, D. C., Castro, L., Churcher, I., Rees, D. C., Thomas, A. W., Wilson, D. M., and Wood, A · 2018
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Diverse beam search: Decoding diverse solutions from neural sequence models, 2018
Vijayakumar, A. K., Cogswell, M., Selvaraju, R. R., Sun, Q., Lee, S., Crandall, D., and Batra, D · 2018
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Learning to make generalizable and diverse predictions for retrosynthesis, 2019
Chen, B., Shen, T., Jaakkola, T. S., and Barzilay, R · 2019
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Rdchiral: An rdkit wrapper for handling stereochemistry in retrosynthetic template extraction and application
Coley, C. W., Green, W. H., and Jensen, K. F · 2019
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Retrosynthesis prediction with conditional graph logic network
Dai, H., Li, C., Coley, C., Dai, B., and Song, L · 2019
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Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction
Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C. A., Bekas, C., and Lee, A. A · 2019
Cited alongside, same era.
Guiding attention for self-supervised learning with transformers
Deshpande, A., and Narasimhan, K · 2020
Cited alongside, same era.
Automatic retrosynthetic route planning using template-free models
Lin, K., Xu, Y., Pei, J., and Lai, L · 2020
Cited alongside, same era.
Predicting retrosynthetic pathways using transformer-based models and a hyper-graph exploration strategy
Schwaller, P., Petraglia, R., Zullo, V., Nair, V. H., Haeuselmann, R. A., Pisoni, R., Bekas, C., Iuliano, A., and Laino, T · 2020
Cited alongside, same era.
A graph to graphs framework for retrosynthesis prediction
Shi, C., Xu, M., Guo, H., Zhang, M., and Tang, J · 2020
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Learning graph models for template-free retrosynthesis, 2020
On the influence of template size, canonicalization and exclusivity for retrosynthesis and reaction prediction applications
Heid, E., Liu, J., Aude, A., and Green, W. H · 2021
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Chemformer: A pre-trained transformer for computational chemistry
Irwin, R., Dimitriadis, S., He, J., and Bjerrum, E. J · 2021
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Valid, plausible, and diverse retrosynthesis using tied two-way transformers with latent variables
Kim, E., Lee, D., Kwon, Y., Park, M. S., and Choi, Y.-S · 2021
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Molecule edit graph attention network: Modeling chemical reactions as sequences of graph edits
Sacha, M., Błaż, M., Byrski, P., Dąbrowski-Tumański, P., Chromiński, M., Loska, R., Włodarczyk-Pruszyński, P., and Jastrzębski, S · 2021
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Extraction of organic chemistry grammar from unsupervised learning of chemical reactions
Schwaller, P., Hoover, B., Reymond, J.-L., Strobelt, H., and Laino, T · 2021
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Somnath, V. R., Bunne, C., Coley, C. W., Krause, A., and Barzilay, R · 2020
Cited alongside, same era.
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
Cited alongside, same era.
Retroxpert: Decompose retrosynthesis prediction like a chemist
Yan, C., Ding, Q., Zhao, P., Zheng, S., YANG, J., Yu, Y., and Huang, J · 2020
Cited alongside, same era.
Predicting retrosynthetic reactions using self-corrected transformer neural networks
Zheng, S., Rao, J., Zhang, Z., Xu, J., and Yang, Y · 2020
Cited alongside, same era.
Molecule attention transformer, 2020
Łukasz Maziarka, Danel, T., Mucha, S., Rataj, K., Tabor, J., and Jastrzębski, S · 2020
Cited alongside, same era.
Learning attributed graph representation with communicative message passing transformer
Chen, J., Zheng, S., Song, Y., Rao, J., and Yang, Y · 2021
Cited alongside, same era.
Deep retrosynthetic reaction prediction using local reactivity and global attention
Chen, S., and Jung, Y · 2021
Cited alongside, same era.
Gta: Graph truncated attention for retrosynthesis
Seo, S.-W., Song, Y. Y., Yang, J. Y., Bae, S., Lee, H., Shin, J., Hwang, S. J., and Yang, E · 2021
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Permutation invariant graph-to-sequence model for template-free retrosynthesis and reaction prediction, 2021
Tu, Z., and Coley, C. W · 2021
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Neuraltpl: a deep learning approach for efficient reaction space exploration
Wan, Y., Li, X., Wang, X., Yao, X., Liao, B., Hsieh, C.-Y., and Zhang, S · 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
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Do transformers really perform bad for graph representation?, 2021
Ying, C., Cai, T., Luo, S., Zheng, S., Ke, G., He, D., Shen, Y., and Liu, T.-Y · 2021
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MG-BERT: leveraging unsupervised atomic representation learning for molecular property prediction
Zhang, X.-C., Wu, C.-K., Yang, Z.-J., Wu, Z.-X., Yi, J.-C., Hsieh, C.-Y., Hou, T.-J., and Cao, D.-S · 2021
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Dual-view molecule pre-training, 2021
Zhu, J., Xia, Y., Qin, T., Zhou, W., Li, H., and Liu, T.-Y · 2021
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Relative molecule self-attention transformer, 2021
Łukasz Maziarka, Majchrowski, D., Danel, T., Gaiński, P., Tabor, J., Podolak, I., Morkisz, P., and Jastrzębski, S · 2021
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