Fetching the paper…
Reading the bibliography…
Retrosynthesis consists of breaking down a chemical compound recursively step-by-step into molecular precursors until a set of commercially available molecules is found with the goal to provide a synthesis route.
E. J. Corey and X.-M. Cheng, The logic of chemical synthesis . New York: John Wiley & Sons, Ltd, 1989
1989
Earlier work this paper cites.
D. Butina, “Unsupervised Data Base Clustering Based on Daylight’s Fingerprint and Tanimoto Similarity: A Fast and Automated Way To Cluster Small and Large Data Sets,” Journal of Chemical Information and Computer Sciences , vol. 39, no. 4, pp. 747–750, 1999, publisher: American Chemical Society. [Online]. Available: https://doi.org/10.1021/ci9803381
1999
Earlier work this paper cites.
2008
Earlier work this paper cites.
D. M. Lowe, “Extraction of chemical structures and reactions from the literature,” Thesis, 2012
2012
Earlier work this paper cites.
N. Schneider, N. Stiefl, and G. A. Landrum, “What’s What: The (Nearly) Definitive Guide to Reaction Role Assignment,” Journal of Chemical Information and Modeling , vol. 56, no. 12, pp. 2336–2346, 2016, publisher: American Chemical Society. [Online]. Available: https://doi.org/10.1021/acs.jcim.6b00564
2016
Earlier work this paper cites.
M. H. Segler and M. P. Waller, “Neural-Symbolic Machine Learning for Retrosynthesis and Reaction Prediction,” Chemistry - A European Journal , vol. 23, no. 25, pp. 5966–5971, 2017. [Online]. Available: https://chemistry-europe.onlinelibrary.wiley.com/doi/abs/10.1002/chem.201605499
2017
Earlier work this paper cites.
D. Silver, J. Schrittwieser, K. Simonyan, I. Antonoglou, A. Huang, A. Guez, T. Hubert, L. Baker, M. Lai, A. Bolton, Y. Chen, T. Lillicrap, F. Hui, L. Sifre, G. Van Den Driessche, T. Graepel, and D. Hassabis, “Mastering the game of Go without human knowledge,” Nature , vol. 550, no. 7676, pp. 354–359, 2017. [Online]. Available: https://doi.org/10.1038/nature24270
2017
Earlier work this paper cites.
M. H. S. Segler, M. Preuss, and M. P. Waller, “Planning chemical syntheses with deep neural networks and symbolic AI,” Nature , vol. 555, no. 7698, pp. 604–610, 2018. [Online]. Available: http://dx.doi.org/10.1038/nature25978
2018
Earlier work this paper cites.
C. W. Coley, W. H. Green, and K. F. Jensen, “Machine Learning in Computer-Aided Synthesis Planning,” Accounts of Chemical Research , vol. 51, no. 5, pp. 1281–1289, 2018, publisher: American Chemical Society. [Online]. Available: https://doi.org/10.1021/acs.accounts.8b00087
2018
Earlier work this paper cites.
C. W. Coley, D. A. Thomas, J. A. Lummiss, J. N. Jaworski, C. P. Breen, V. Schultz, T. Hart, J. S. Fishman, L. Rogers, H. Gao, R. W. Hicklin, P. P. Plehiers, J. Byington, J. S. Piotti, W. H. Green, A. John Hart, T. F. Jamison, and K. F. Jensen, “A robotic platform for flow synthesis of organic compounds informed by AI planning,” Science , vol. 365, no. 6453, p. eaax1566, 2019. [Online]. Available: https://www.science.org/doi/abs/10.1126/science.aax1566
2019
Earlier work this paper cites.
A. Kishimoto, B. Buesser, B. Chen, and A. Botea, “Depth-First Proof-Number Search with Heuristic Edge Cost and Application to Chemical Synthesis Planning,” in Advances in Neural Information Processing Systems , H. Wallach, H. Larochelle, A. Beygelzimer, F. Alche-Buc, E. Fox, and R. Garnett, Eds., vol. 32. Curran Associates, Inc., 2019. [Online]. Available: https://proceedings.neurips.cc/paper/2019/file/4fc28b7093b135c21c7183ac07e928a6-Paper.pdf
2019
Earlier work this paper cites.
J. S. Schreck, C. W. Coley, and K. J. M. Bishop, “Learning Retrosynthetic Planning through Simulated Experience,” ACS Central Science , vol. 5, no. 6, pp. 970–981, 2019, publisher: American Chemical Society. [Online]. Available: https://doi.org/10.1021/acscentsci.9b00055
2019
Earlier work this paper cites.
N. Brown, M. Fiscato, M. H. Segler, and A. C. Vaucher, “GuacaMol: Benchmarking Models for de Novo Molecular Design,” Journal of Chemical Information and Modeling , vol. 59, no. 3, pp. 1096–1108, 2019, publisher: American Chemical Society. [Online]. Available: https://doi.org/10.1021/acs.jcim.8b00839
2019
Earlier work this paper cites.
C. W. Coley, W. H. Green, and K. F. Jensen, “RDChiral: An RDKit Wrapper for Handling Stereochemistry in Retrosynthetic Template Extraction and Application,” Journal of Chemical Information and Modeling , vol. 59, no. 6, pp. 2529–2537, 2019, publisher: American Chemical Society. [Online]. Available: https://doi.org/10.1021/acs.jcim.9b00286
2019
Earlier work this paper cites.
H. Dai, C. Li, C. Coley, B. Dai, and L. Song, “Retrosynthesis Prediction with Conditional Graph Logic Network,” in Advances in Neural Information Processing Systems , H. Wallach, H. Larochelle, A. Beygelzimer, F. d’Alché Buc, E. Fox, and R. Garnett, Eds., vol. 32. Curran Associates, Inc., 2019. [Online]. Available: https://proceedings.neurips.cc/paper_files/paper/2019/file/0d2b2061826a5df3221116a5085a6052-Paper.pdf
2019
Earlier work this paper cites.
I. V. Tetko, P. Karpov, R. Van Deursen, and G. Godin, “State-of-the-art augmented NLP transformer models for direct and single-step retrosynthesis,” Nature Communications , vol. 11, no. 1, p. 5575, 2020. [Online]. Available: https://doi.org/10.1038/s41467-020-19266-y
2020
Earlier work this paper cites.
C. Shi, M. Xu, H. Guo, M. Zhang, and J. Tang, “A Graph to Graphs Framework for Retrosynthesis Prediction,” in Proceedings of the 37th International Conference on Machine Learning , ser. Proceedings of Machine Learning Research, H. D. III and A. Singh, Eds., vol. 119. PMLR, 2020, pp. 8818–8827. [Online]. Available: https://proceedings.mlr.press/v119/shi20d.html
2020
Earlier work this paper cites.
B. Chen, C. Li, H. Dai, and L. Song, “Retro*: Learning Retrosynthetic Planning with Neural Guided A* Search,” in Proceedings of the 37th International Conference on Machine Learning , ser. Proceedings of Machine Learning Research, H. D. III and A. Singh, Eds., vol. 119. PMLR, 2020, pp. 1608–1616. [Online]. Available: https://proceedings.mlr.press/v119/chen20k.html
2020
Cited alongside, same era.
K. Lin, Y. Xu, J. Pei, and L. Lai, “Automatic retrosynthetic route planning using template-free models,” Chem. Sci. , vol. 11, no. 12, pp. 3355–3364, 2020, publisher: The Royal Society of Chemistry. [Online]. Available: http://dx.doi.org/10.1039/C9SC03666K
2020
Cited alongside, same era.
P. Schwaller, R. Petraglia, V. Zullo, V. H. Nair, R. A. Haeuselmann, R. Pisoni, C. Bekas, A. Iuliano, and T. Laino, “Predicting retrosynthetic pathways using transformer-based models and a hyper-graph exploration strategy,” Chem. Sci. , vol. 11, no. 12, pp. 3316–3325, 2020, publisher: The Royal Society of Chemistry. [Online]. Available: http://dx.doi.org/10.1039/C9SC05704H
2020
Cited alongside, same era.
D. Kreutter and J.-L. Reymond, “Multistep retrosynthesis combining a disconnection aware triple transformer loop with a route penalty score guided tree search,” 2023. [Online]. Available: https://doi.org/10.26434/chemrxiv-2022-8khth-v2
2022
Later among the works it cites.
Y. Yu, Y. Wei, K. Kuang, Z. Huang, H. Yao, and F. Wu, “GRASP: Navigating Retrosynthetic Planning with Goal-driven Policy,” in Advances in neural information processing systems , S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh, Eds., vol. 35. Curran Associates, Inc., 2022, pp. 10 257–10 268. [Online]. Available: https://proceedings.neurips.cc/paper_files/paper/2022/hash/42beaab8aa8da1c77581609a61eced93-Abstract-Conference.html
2022
Later among the works it cites.
H. Tu, S. Shorewala, T. Ma, and V. Thost, “Retrosynthesis Prediction Revisited,” in NeurIPS 2022 AI for Science: Progress and Promises , 2022. [Online]. Available: https://openreview.net/forum?id=kLzFuf4GoC-
2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A. Thakkar, T. Kogej, J.-L. Reymond, O. Engkvist, and E. J. Bjerrum, “Datasets and their influence on the development of computer assisted synthesis planning tools in the pharmaceutical domain,” Chem. Sci. , vol. 11, no. 1, pp. 154–168, 2020. [Online]. Available: http://dx.doi.org/10.1039/C9SC04944D
2020
Cited alongside, same era.
S. Genheden, A. Thakkar, V. Chadimová, J.-L. Reymond, O. Engkvist, and E. Bjerrum, “AiZynthFinder: a fast, robust and flexible open-source software for retrosynthetic planning,” Journal of Cheminformatics , vol. 12, no. 1, p. 70, 2020. [Online]. Available: https://doi.org/10.1186/s13321-020-00472-1
2020
Cited alongside, same era.
M. Lewis, Y. Liu, N. Goyal, M. Ghazvininejad, A. Mohamed, O. Levy, V. Stoyanov, and L. Zettlemoyer, “BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension,” in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics . Association for Computational Linguistics, 2020, pp. 7871–7880. [Online]. Available: https://aclanthology.org/2020.acl-main.703
2020
Cited alongside, same era.
F. Miljković, R. Rodríguez-Pérez, and J. Bajorath, “Impact of Artificial Intelligence on Compound Discovery, Design, and Synthesis,” ACS Omega , vol. 6, no. 49, pp. 33 293–33 299, 2021, publisher: American Chemical Society. [Online]. Available: https://doi.org/10.1021/acsomega.1c05512
2021
Cited alongside, same era.
S. Chen and Y. Jung, “Deep Retrosynthetic Reaction Prediction using Local Reactivity and Global Attention,” JACS Au , vol. 1, no. 10, pp. 1612–1620, 2021, publisher: American Chemical Society. [Online]. Available: https://doi.org/10.1021/jacsau.1c00246
2021
Cited alongside, same era.
V. R. Somnath, C. Bunne, C. W. Coley, A. Krause, and R. Barzilay, “Learning Graph Models for Retrosynthesis Prediction,” in Advances in Neural Information Processing Systems , A. Beygelzimer, Y. Dauphin, P. Liang, and J. Wortman Vaughan, Eds., 2021. [Online]. Available: https://openreview.net/forum?id=SnONpXZ_uQ_
2021
Cited alongside, same era.
X. Wang, Y. Li, J. Qiu, G. Chen, H. Liu, B. Liao, C.-Y. Hsieh, and X. Yao, “RetroPrime: A Diverse, plausible and Transformer-based method for Single-Step retrosynthesis predictions,” Chemical Engineering Journal , vol. 420, p. 129845, 2021. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1385894721014303
2021
Cited alongside, same era.
P. Schwaller, B. Hoover, J.-L. Reymond, H. Strobelt, and T. Laino, “Extraction of organic chemistry grammar from unsupervised learning of chemical reactions,” Science Advances , vol. 7, no. 15, p. eabe4166, 2021. [Online]. Available: https://www.science.org/doi/abs/10.1126/sciadv.abe4166
2021
Cited alongside, same era.
S. Genheden, O. Engkvist, and E. Bjerrum, “Clustering of Synthetic Routes Using Tree Edit Distance,” Journal of Chemical Information and Modeling , vol. 61, no. 8, pp. 3899–3907, 2021, publisher: American Chemical Society. [Online]. Available: https://doi.org/10.1021/acs.jcim.1c00232
2021
Cited alongside, same era.
A. K. Hassen, P. Torren-Peraire, S. Genheden, J. Verhoeven, M. Preuss, and I. V. Tetko, “Mind the retrosynthesis gap: Bridging the divide between single-step and multi-step retrosynthesis prediction,” in NeurIPS 2022 AI for Science: Progress and Promises , 2022. [Online]. Available: https://openreview.net/forum?id=LjdtY0hM7tf
2022
Later among the works it cites.
S. Genheden and E. Bjerrum, “PaRoutes: towards a framework for benchmarking retrosynthesis route predictions,” Digital Discovery , vol. 1, no. 4, pp. 527–539, 2022, publisher: RSC. [Online]. Available: http://dx.doi.org/10.1039/D2DD00015F
2022
Later among the works it cites.
A. Tripp, K. Maziarz, S. Lewis, G. Liu, and M. Segler, “Re-Evaluating Chemical Synthesis Planning Algorithms,” in NeurIPS 2022 AI for Science: Progress and Promises , 2022. [Online]. Available: https://openreview.net/forum?id=8VLeT8DFeD
2022
Later among the works it cites.
——, “Fast prediction of distances between synthetic routes with deep learning,” Machine Learning: Science and Technology , vol. 3, no. 1, p. 015018, 2022, publisher: IOP Publishing. [Online]. Available: https://dx.doi.org/10.1088/2632-2153/ac4a91
2022
Later among the works it cites.
2023
Closest in time.
2023
Closest in time.
S. Genheden, P.-O. Norrby, and O. Engkvist, “AiZynthTrain: Robust, Reproducible, and Extensible Pipelines for Training Synthesis Prediction Models,” Journal of Chemical Information and Modeling , vol. 63, no. 7, pp. 1841–1846, 2023, publisher: American Chemical Society. [Online]. Available: https://doi.org/10.1021/acs.jcim.2c01486
2023
Closest in time.
O. J. M. Béquignon, B. J. Bongers, W. Jespers, A. P. IJzerman, B. van der Water, and G. J. P. van Westen, “Papyrus: a large-scale curated dataset aimed at bioactivity predictions,” Journal of Cheminformatics , vol. 15, no. 1, p. 3, 2023. [Online]. Available: https://doi.org/10.1186/s13321-022-00672-x
2023
Closest in time.
Elsevier Limited, “Reaxys,” 2023. [Online]. Available: https://www.reaxys.com/
2023
Closest in time.
NextMove Software, “Pistachio,” 2023. [Online]. Available: https://www.nextmovesoftware.com/pistachio.html
2023
Closest in time.
Enamine Ltd., “Enamine Building Blocks Catalog,” 2023. [Online]. Available: https://enamine.net/building-blocks/building-blocks-catalog
2023
Closest in time.
Molport SIA, “Moldport Compound Sourcing, Selling and Purchasing Platform,” 2023. [Online]. Available: https://www.molport.com/shop/index
2023
Closest in time.
eMolecules, Inc., “eMolecules Chemical Building Blocks,” 2023. [Online]. Available: https://www.emolecules.com/products/building-blocks
2023
Closest in time.