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Retrosynthesis, which aims to find a route to synthesize a target molecule from commercially available starting materials, is a critical task in drug discovery and materials design.
Multi-armed bandits with episode context
Rosin, C. D · 2011
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Extraction of chemical structures and reactions from the literature
Lowe, D. M · 2012
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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Sequence to sequence learning with neural networks
Sutskever, I., Vinyals, O., and Le, Q. V · 2014
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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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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
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Chemical synthesis planning with tree search and deep neural network policies
Segler, M., Preuß, M., and Waller, M. P · 2017
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Neural-symbolic machine learning for retrosynthesis and reaction prediction
Segler, M. H. and Waller, M. P · 2017
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Mastering the game of Go without human knowledge
Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., et al · 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
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Planning chemical syntheses with deep neural networks and symbolic AI
Segler, M. H. S., Preuss, M., and Waller, M. P · 2018
Cited alongside, same era.
A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play
Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., Lanctot, M., Sifre, L., Kumaran, D., Graepel, T., et al · 2018
Cited alongside, same era.
Reinforcement learning: An introduction
Sutton, R. S. and Barto, A. G · 2018
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Guacamol: benchmarking models for de novo molecular design
Brown, N., Fiscato, M., Segler, M. H., and Vaucher, A. C · 2019
Cited alongside, same era.
Retrosynthesis prediction with conditional graph logic network
Dai, H., Li, C., Coley, C., Dai, B., and Song, L · 2019
Cited alongside, same era.
Depth-first proof-number search with heuristic edge cost and application to chemical synthesis planning
Deep retrosynthetic reaction prediction using local reactivity and global attention
Chen, S. and Jung, Y · 2021
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Deep learning in retrosynthesis planning: datasets, models and tools
Dong, J., Zhao, M., Liu, Y., Su, Y., and Zeng, X · 2021
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Self-improved retrosynthetic planning
Kim, J., Ahn, S., Lee, H., and Shin, J · 2021
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Paroutes: towards a framework for benchmarking retrosynthesis route predictions
Genheden, S. and Bjerrum, E · 2022
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Gnn-retro: Retrosynthetic planning with graph neural networks
Han, P., Zhao, P., Lu, C., Huang, J., Wu, J., Shang, S., Yao, B., and Zhang, X · 2022
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Mind the retrosynthesis gap: Bridging the divide between single-step and multi-step retrosynthesis prediction
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Kishimoto, A., Buesser, B., Chen, B., and Botea, A · 2019
Cited alongside, same era.
Learning retrosynthetic planning through simulated experience
Schreck, J. S., Coley, C. W., and Bishop, K. J. M · 2019
Cited alongside, same era.
Retro*: learning retrosynthetic planning with neural guided A* search
Chen, B., Li, C., Dai, H., and Song, L · 2020
Cited alongside, same era.
Demonstration actor critic
Liu, G., Zhao, L., Zhang, P., Bian, J., Qin, T., Yu, N., and Liu, T.-Y · 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.
Hassen, A. K., Torren-Peraire, P., Genheden, S., Verhoeven, J., Preuss, M., and Tetko, I. V · 2022
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Re-evaluating chemical synthesis planning algorithms
Tripp, A., Maziarz, K., Lewis, S., Liu, G., and Segler, M · 2022
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Retrosynthesis prediction revisited
Tu, H., Shorewala, S., Ma, T., and Thost, V · 2022
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Retrograph: Retrosynthetic planning with graph search
Xie, S., Yan, R., Han, P., Xia, Y., Wu, L., Guo, C., Yang, B., and Qin, T · 2022
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GRASP: Navigating retrosynthetic planning with goal-driven policy
Yu, Y., Wei, Y., Kuang, K., Huang, Z., Yao, H., and Wu, F · 2048
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