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Automated Synthesis Planning has recently re-emerged as a research area at the intersection of chemistry and machine learning.
Concerning one system of classification and codification of organic reactions
Vleduts, G · 1963
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Computer-assisted design of complex organic syntheses: Pathways for molecular synthesis can be devised with a computer and equipment for graphical communication
Corey, E. J. and Wipke, W. T · 1969
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Computer-assisted planning of organic syntheses: the second generation of programs
Ihlenfeldt, W.-D. and Gasteiger, J · 1996
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Computer-aided organic synthesis
Todd, M. H · 2005
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
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Wirkstoffdesign: Entwurf und Wirkung von Arzneistoffen
Klebe, G · 2009
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Reducibility among combinatorial problems
Karp, R. M · 2010
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Computer-aided synthesis design: 40 years on
Cook, A., Johnson, A. P., Law, J., Mirzazadeh, M., Ravitz, O., and Simon, A · 2012
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Extraction of chemical structures and reactions from the literature
Lowe, D. M · 2012
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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 synthetic planning: the end of the beginning
Szymkuć, S., Gajewska, E. P., Klucznik, T., Molga, K., Dittwald, P., Startek, M., Bajczyk, M., and Grzybowski, B. A · 2016
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 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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Pistachio: Search and faceting of large reaction databases
Mayfield, J., Lowe, D., and Sayle, R · 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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Generating focussed molecule libraries for drug discovery with recurrent neural networks
Segler, M. H., Kogej, T., Tyrchan, C., and Waller, M. P · 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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Automatic chemical design using a data-driven continuous representation of molecules
Gómez-Bombarelli, R., Wei, J. N., Duvenaud, D., Hernández-Lobato, J. M., Sánchez-Lengeling, B., Sheberla, D., Aguilera-Iparraguirre, J., Hirzel, T. D., Adams, R. P., and Aspuru-Guzik, A · 2018
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Deep reinforcement learning that matters
Henderson, P., Islam, R., Bachman, P., Pineau, J., Precup, D., and Meger, D · 2018
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Efficient syntheses of diverse, medicinally relevant targets planned by computer and executed in the laboratory
Klucznik, T., Mikulak-Klucznik, B., McCormack, M. P., Lima, H., Szymkuć, S., Bhowmick, M., Molga, K., Zhou, Y., Rickershauser, L., Gajewska, E. P., et al · 2018
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Planning chemical syntheses with deep neural networks and symbolic ai
Segler, M. H., Preuss, M., and Waller, M. P · 2018
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Optuna: A next-generation hyperparameter optimization framework
Akiba, T., Sano, S., Yanase, T., Ohta, T., and Koyama, M · 2019
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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
Kishimoto, A., Buesser, B., Chen, B., and Botea, A · 2019
Cited alongside, same era.
Evaluation metrics for single-step retrosynthetic models
Schwaller, P., Nair, V. H., Petraglia, R., and Laino, T · 2019
Cited alongside, same era.
Barking up the right tree: an approach to search over molecule synthesis dags
Bradshaw, J., Paige, B., Kusner, M. J., Segler, M., and Hernández-Lobato, J. M · 2020
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.
Aizynthfinder: a fast, robust and flexible open-source software for retrosynthetic planning
Chemformer: a pre-trained transformer for computational chemistry
Irwin, R., Dimitriadis, S., He, J., and Bjerrum, E. J · 2022
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Improving the performance of models for one-step retrosynthesis through re-ranking
Lin, M. H., Tu, Z., and Coley, C. W · 2022
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Learning to extend molecular scaffolds with structural motifs
Maziarz, K., Jackson-Flux, H. R., Cameron, P., Sirockin, F., Schneider, N., Stiefl, N., Segler, M., and Brockschmidt, M · 2022
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Machine intelligence for chemical reaction space
Schwaller, P., Vaucher, A. C., Laplaza, R., Bunne, C., Krause, A., Corminboeuf, C., and Laino, T · 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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Genheden, S., Thakkar, A., Chadimová, V., Reymond, J.-L., Engkvist, O., and Bjerrum, E · 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.
Machine learning the ropes: principles, applications and directions in synthetic chemistry
Strieth-Kalthoff, F., Sandfort, F., Segler, M. H., and Glorius, F · 2020
Cited alongside, same era.
Energy-based view of retrosynthesis
Sun, R., Dai, H., Li, L., Kearnes, S., and Dai, B · 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.
Datasets and their influence on the development of computer assisted synthesis planning tools in the pharmaceutical domain
Thakkar, A., Kogej, T., Reymond, J.-L., Engkvist, O., and Bjerrum, E. J · 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.
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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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Retrocomposer: Composing templates for template-based retrosynthesis prediction
Yan, C., Zhao, P., Lu, C., Yu, Y., and Huang, J · 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 · 2022
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Root-aligned smiles: a tight representation for chemical reaction prediction
Zhong, Z., Song, J., Feng, Z., Liu, T., Jia, L., Yao, S., Wu, M., Hou, T., and Song, M · 2022
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Learning chemical rules of retrosynthesis with pre-training
Jiang, Y., Ying, W., Wu, F., Huang, Z., Kuang, K., and Wang, Z · 2023
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Retrosynthetic planning with dual value networks
Liu, G., Xue, D., Xie, S., Xia, Y., Tripp, A., Maziarz, K., Segler, M., Qin, T., Zhang, Z., and Liu, T.-Y · 2023
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Fake it until you make it? generative de novo design and virtual screening of synthesizable molecules
Stanley, M. and Segler, M · 2023
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Models matter: The impact of single-step retrosynthesis on synthesis planning
Torren-Peraire, P., Hassen, A. K., Genheden, S., Verhoeven, J., Clevert, D.-A., Preuss, M., and Tetko, I · 2023
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Retro-fallback: retrosynthetic planning in an uncertain world
Tripp, A., Maziarz, K., Lewis, S., Segler, M., and Hernández-Lobato, J. M · 2023
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Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery
Tu, Z., Stuyver, T., and Coley, C. W · 2023
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Retrosynthesis prediction with an interpretable deep-learning framework based on molecular assembly tasks
Wang, Y., Pang, C., Wang, Y., Jin, J., Zhang, J., Zeng, X., Su, R., Zou, Q., and Wei, L · 2023
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Orderly: Datasets and benchmarks for chemical reaction data
Wigh, D., Arrowsmith, J., Pomberger, A., Felton, K., and Lapkin, A · 2023
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Improve retrosynthesis planning with a molecular editing language
Xiong, J., Zhang, W., Fu, Z., Huang, J., Kong, X., Wang, Y., Xiong, Z., and Zheng, M · 2023
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Recent advances in artificial intelligence for retrosynthesis
Zhong, Z., Song, J., Feng, Z., Liu, T., Jia, L., Yao, S., Hou, T., and Song, M · 2023
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Retrogfn: Diverse and feasible retrosynthesis using gflownets
Gaiński, P., Koziarski, M., Maziarz, K., Segler, M., Tabor, J., and Śmieja, M · 2024
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Retrobridge: Modeling retrosynthesis with markov bridges
Igashov, I., Schneuing, A., Segler, M., Bronstein, M. M., and Correia, B · 2024
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