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Retrosynthesis is the task of planning a series of chemical reactions to create a desired molecule from simpler, buyable molecules.
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Development of a novel fingerprint for chemical reactions and its application to large-scale reaction classification and similarity
Nadine Schneider, Daniel M Lowe, Roger A Sayle, and Gregory A Landrum · 2015
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Neural-symbolic machine learning for retrosynthesis and reaction prediction
Marwin HS Segler and Mark P Waller · 2017
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Scscore: synthetic complexity learned from a reaction corpus
Connor W Coley, Luke Rogers, William H Green, and Klavs F Jensen · 2018
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Planning chemical syntheses with deep neural networks and symbolic ai
Marwin HS Segler, Mike Preuss, and Mark P Waller · 2018
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A model to search for synthesizable molecules
John Bradshaw, Brooks Paige, Matt J Kusner, Marwin Segler, and José Miguel Hernández-Lobato · 2019
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Guacamol: benchmarking models for de novo molecular design
Amortized tree generation for bottom-up synthesis planning and synthesizable molecular design
Wenhao Gao, Rocío Mercado, and Connor W Coley · 2021
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Self-improved retrosynthetic planning
Junsu Kim, Sungsoo Ahn, Hankook Lee, and Jinwoo Shin · 2021
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Molecule edit graph attention network: modeling chemical reactions as sequences of graph edits
Mikołaj Sacha, Mikołaj Błaz, Piotr Byrski, Paweł Dabrowski-Tumanski, Mikołaj Chrominski, Rafał Loska, Paweł Włodarczyk-Pruszynski, and Stanisław Jastrzebski · 2021
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Modern hopfield networks for few-and zero-shot reaction template prediction
Philipp Seidl, Philipp Renz, Natalia Dyubankova, Paulo Neves, Jonas Verhoeven, Marwin Segler, Jörg K Wegner, Sepp Hochreiter, and Günter Klambauer · 2021
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Retrosynthetic accessibility score (rascore)–rapid machine learned synthesizability classification from ai driven retrosynthetic planning
Amol Thakkar, Veronika Chadimová, Esben Jannik Bjerrum, Ola Engkvist, and Jean-Louis Reymond · 2021
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Nathan Brown, Marco Fiscato, Marwin HS Segler, and Alain C Vaucher · 2019
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Retrosynthesis prediction with conditional graph logic network
Hanjun Dai, Chengtao Li, Connor Coley, Bo Dai, and Le Song · 2019
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Depth-first proof-number search with heuristic edge cost and application to chemical synthesis planning
Akihiro Kishimoto, Beat Buesser, Bei Chen, and Adi Botea · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
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Barking up the right tree: an approach to search over molecule synthesis dags
John Bradshaw, Brooks Paige, Matt J Kusner, Marwin Segler, and José Miguel Hernández-Lobato · 2020
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Retro*: learning retrosynthetic planning with neural guided a* search
Binghong Chen, Chengtao Li, Hanjun Dai, and Le Song · 2020
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Aizynthfinder: a fast, robust and flexible open-source software for retrosynthetic planning
Samuel Genheden, Amol Thakkar, Veronika Chadimová, Jean-Louis Reymond, Ola Engkvist, and Esben Bjerrum · 2020
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Chemformer: a pre-trained transformer for computational chemistry
Ross Irwin, Spyridon Dimitriadis, Jiazhen He, and Esben Jannik Bjerrum · 2022
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Prediction of compound synthesis accessibility based on reaction knowledge graph
Baiqing Li and Hongming Chen · 2022
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Retrognn: fast estimation of synthesizability for virtual screening and de novo design by learning from slow retrosynthesis software
Cheng-Hao Liu, Maksym Korablyov, Stanisław Jastrzebski, Paweł Włodarczyk-Pruszynśki, Yoshua Bengio, and Marwin Segler · 2022
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Retrograph: Retrosynthetic planning with graph search
Shufang Xie, Rui Yan, Peng Han, Yingce Xia, Lijun Wu, Chenjuan Guo, Bin Yang, and Tao Qin · 2022
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Grasp: Navigating retrosynthetic planning with goal-driven policy
Yemin Yu, Ying Wei, Kun Kuang, Zhengxing Huang, Huaxiu Yao, and Fei Wu · 2022
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Root-aligned smiles: a tight representation for chemical reaction prediction
Zipeng Zhong, Jie Song, Zunlei Feng, Tiantao Liu, Lingxiang Jia, Shaolun Yao, Min Wu, Tingjun Hou, and Mingli Song · 2022
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Rethink reporting of evaluation results in ai
Ryan Burnell, Wout Schellaert, John Burden, Tomer D Ullman, Fernando Martinez-Plumed, Joshua B Tenenbaum, Danaja Rutar, Lucy G Cheke, Jascha Sohl-Dickstein, Melanie Mitchell, et al · 2023
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Re-evaluating retrosynthesis algorithms with syntheseus
Krzysztof Maziarz, Austin Tripp, Guoqing Liu, Megan Stanley, Shufang Xie, Piotr Gaiński, Philipp Seidl, and Marwin Segler · 2023
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Critical assessment of synthetic accessibility scores in computer-assisted synthesis planning
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Fake it until you make it? generative de novo design and virtual screening of synthesizable molecules
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Tanimoto random features for scalable molecular machine learning
Austin Tripp, Sergio Bacallado, Sukriti Singh, and José Miguel Hernández-Lobato · 2023
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Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery
Zhengkai Tu, Thijs Stuyver, and Connor W Coley · 2023
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Retrosynthesis prediction using an end-to-end graph generative architecture for molecular graph editing
Weihe Zhong, Ziduo Yang, and Calvin Yu-Chian Chen · 2023
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