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Learning to make generalizable and diverse predictions for retrosynthesis. arXiv preprint arXiv:1910.09688 ( 2019 ). Benson Chen, Tianxiao Shen, Tommi S Jaakkola, and Regina Barzilay. 2019. Learning to make generalizable and diverse predictions for retrosynthesis. arXiv preprint arXiv:1910.09688 (2019)."},{"key":"e_1_3_2_1_3_1","volume-title":"Deep Retrosynthetic Reaction Prediction using Local Reactivity and Global Attention. JACS Au","author":"Chen Shuan","year":"2021","unstructured":"Shuan Chen and Yousung Jung . 2021. Deep Retrosynthetic Reaction Prediction using Local Reactivity and Global Attention. JACS Au ( 2021 ). Shuan Chen and Yousung Jung. 2021. Deep Retrosynthetic Reaction Prediction using Local Reactivity and Global Attention. JACS Au (2021)."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/W14-4012"},{"key":"e_1_3_2_1_5_1","volume-title":"Computer-assisted retrosynthesis based on molecular similarity. 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Retrosynthesis Prediction with Conditional Graph Logic Network . In Proc. of NeurIPS. Hanjun Dai, Chengtao Li, Connor Coley, Bo Dai, and Le Song. 2019a. Retrosynthesis Prediction with Conditional Graph Logic Network. In Proc. of NeurIPS."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1285"},{"key":"e_1_3_2_1_9_1","volume-title":"Retrosynthesis with attention-based NMT model and chemical analysis of ''wrong\" predictions. RSC advances","author":"Duan Hongliang","year":"2020","unstructured":"Hongliang Duan , Ling Wang , Chengyun Zhang , Lin Guo , and Jianjun Li. 2020. Retrosynthesis with attention-based NMT model and chemical analysis of ''wrong\" predictions. RSC advances ( 2020 ). Hongliang Duan, Ling Wang, Chengyun Zhang, Lin Guo, and Jianjun Li. 2020. Retrosynthesis with attention-based NMT model and chemical analysis of ''wrong\" predictions. RSC advances (2020)."},{"key":"e_1_3_2_1_10_1","volume-title":"Goodman","author":"Gershman Samuel","year":"2014","unstructured":"Samuel Gershman and Noah D . Goodman . 2014 . Amortized Inference in Probabilistic Reasoning. In Proc. of CogSci . Samuel Gershman and Noah D. Goodman. 2014. Amortized Inference in Probabilistic Reasoning. In Proc. of CogSci."},{"key":"e_1_3_2_1_11_1","unstructured":"Emil Julius Gumbel. 1954. Statistical theory of extreme values and some practical applications: a series of lectures. US Government Printing Office.  Emil Julius Gumbel. 1954. Statistical theory of extreme values and some practical applications: a series of lectures. US Government Printing Office."},{"key":"e_1_3_2_1_12_1","volume-title":"Bridging Nonlinearities and Stochastic Regularizers with Gaussian Error Linear Units. arXiv preprint arXiv:1606.08415","author":"Hendrycks Dan","year":"2016","unstructured":"Dan Hendrycks and Kevin Gimpel . 2016. Bridging Nonlinearities and Stochastic Regularizers with Gaussian Error Linear Units. arXiv preprint arXiv:1606.08415 ( 2016 ). Dan Hendrycks and Kevin Gimpel. 2016. Bridging Nonlinearities and Stochastic Regularizers with Gaussian Error Linear Units. arXiv preprint arXiv:1606.08415 (2016)."},{"key":"e_1_3_2_1_13_1","volume-title":"Proc. of ICLR.","author":"Hu Weihua","year":"2020","unstructured":"Weihua Hu , Bowen Liu , Joseph Gomes , Marinka Zitnik , Percy Liang , Vijay S. Pande , and Jure Leskovec . 2020 . Strategies for Pre-training Graph Neural Networks . In Proc. of ICLR. Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay S. Pande, and Jure Leskovec. 2020. Strategies for Pre-training Graph Neural Networks. In Proc. of ICLR."},{"key":"e_1_3_2_1_14_1","volume-title":"Chemformer: A Pre-Trained Transformer for Computational Chemistry.","author":"Irwin R.","year":"2021","unstructured":"R. Irwin , S. Dimitriadis , J. He , and E. Bjerrum . 2021 . Chemformer: A Pre-Trained Transformer for Computational Chemistry. R. Irwin, S. Dimitriadis, J. He, and E. Bjerrum. 2021. Chemformer: A Pre-Trained Transformer for Computational Chemistry."},{"key":"e_1_3_2_1_15_1","volume-title":"Proc. of ICLR.","author":"Jang Eric","year":"2017","unstructured":"Eric Jang , Shixiang Gu , and Ben Poole . 2017 . Categorical Reparameterization with Gumbel-Softmax . In Proc. of ICLR. Eric Jang, Shixiang Gu, and Ben Poole. 2017. Categorical Reparameterization with Gumbel-Softmax. In Proc. of ICLR."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"crossref","unstructured":"Wojciech Jaworski Sara Szymku\u0107 Barbara Mikulak-Klucznik Krzysztof Piecuch Tomasz Klucznik Micha\u0142 Ka\u017amierowski Jan Rydzewski Anna Gambin Bartosz A Grzybowski etal 2019. Automatic mapping of atoms across both simple and complex chemical reactions. Nat. Comm. (2019).  Wojciech Jaworski Sara Szymku\u0107 Barbara Mikulak-Klucznik Krzysztof Piecuch Tomasz Klucznik Micha\u0142 Ka\u017amierowski Jan Rydzewski Anna Gambin Bartosz A Grzybowski et al. 2019. Automatic mapping of atoms across both simple and complex chemical reactions. Nat. Comm. (2019).","DOI":"10.1038\/s41467-019-09440-2"},{"key":"e_1_3_2_1_17_1","volume-title":"Proc. of ICML.","author":"Jin Wengong","year":"2018","unstructured":"Wengong Jin , Regina Barzilay , and Tommi Jaakkola . 2018 . Junction tree variational autoencoder for molecular graph generation . In Proc. of ICML. Wengong Jin, Regina Barzilay, and Tommi Jaakkola. 2018. Junction tree variational autoencoder for molecular graph generation. In Proc. of ICML."},{"key":"e_1_3_2_1_18_1","volume-title":"Jaakkola","author":"Jin Wengong","year":"2017","unstructured":"Wengong Jin , Connor W. Coley , Regina Barzilay , and Tommi S . Jaakkola . 2017 . Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network. In Proc. of NeurIPS. Wengong Jin, Connor W. Coley, Regina Barzilay, and Tommi S. Jaakkola. 2017. Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network. In Proc. of NeurIPS."},{"key":"e_1_3_2_1_19_1","volume-title":"Min Sik Park, and Youn-Suk Choi","author":"Kim Eunji","year":"2021","unstructured":"Eunji Kim , Dongseon Lee , Youngchun Kwon , Min Sik Park, and Youn-Suk Choi . 2021 . Valid, Plausible , and Diverse Retrosynthesis Using Tied Two-Way Transformers with Latent Variables. J. Chem. Inf. Model . (2021). Eunji Kim, Dongseon Lee, Youngchun Kwon, Min Sik Park, and Youn-Suk Choi. 2021. Valid, Plausible, and Diverse Retrosynthesis Using Tied Two-Way Transformers with Latent Variables. J. Chem. Inf. Model. (2021)."},{"key":"e_1_3_2_1_20_1","volume-title":"Proc. of ICLR.","author":"Kingma Diederick P","year":"2015","unstructured":"Diederick P Kingma and Jimmy Ba . 2015 . Adam: A method for stochastic optimization . In Proc. of ICLR. Diederick P Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In Proc. of ICLR."},{"key":"e_1_3_2_1_21_1","volume-title":"Kingma and Max Welling","author":"Diederik","year":"2014","unstructured":"Diederik P. Kingma and Max Welling . 2014 . Auto-Encoding Variational Bayes. In Proc. of ICLR. Diederik P. Kingma and Max Welling. 2014. Auto-Encoding Variational Bayes. In Proc. of ICLR."},{"key":"e_1_3_2_1_22_1","unstructured":"Kangjie Lin Youjun Xu Jianfeng Pei and Luhua Lai. 2020. Automatic retrosynthetic route planning using template-free models. Chem. Sci. (2020).  Kangjie Lin Youjun Xu Jianfeng Pei and Luhua Lai. 2020. Automatic retrosynthetic route planning using template-free models. Chem. Sci. 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ACS central science (2017)."},{"key":"e_1_3_2_1_24_1","volume-title":"RetroGNN: Approximating Retrosynthesis by Graph Neural Networks for De Novo Drug Design. arXiv preprint arXiv:2011.13042","author":"Liu Cheng-Hao","year":"2020","unstructured":"Cheng-Hao Liu , Maksym Korablyov , Stanis\u0142aw Jastrzk\u0229;bski, Pawe\u0142 W\u0142odarczyk-Pruszy\u0144ski , Yoshua Bengio , and Marwin HS Segler . 2020. RetroGNN: Approximating Retrosynthesis by Graph Neural Networks for De Novo Drug Design. arXiv preprint arXiv:2011.13042 ( 2020 ). Cheng-Hao Liu, Maksym Korablyov, Stanis\u0142aw Jastrzk\u0229;bski, Pawe\u0142 W\u0142odarczyk-Pruszy\u0144ski, Yoshua Bengio, and Marwin HS Segler. 2020. RetroGNN: Approximating Retrosynthesis by Graph Neural Networks for De Novo Drug Design. arXiv preprint arXiv:2011.13042 (2020)."},{"key":"e_1_3_2_1_25_1","volume-title":"Proc. of NeurIPS.","author":"Maddison Chris J","year":"2014","unstructured":"Chris J Maddison , Daniel Tarlow , and Tom Minka . 2014 . A* Sampling . In Proc. of NeurIPS. Chris J Maddison, Daniel Tarlow, and Tom Minka. 2014. A* Sampling. In Proc. of NeurIPS."},{"key":"e_1_3_2_1_26_1","volume-title":"Molecular graph enhanced transformer for retrosynthesis prediction. Neurocomputing","author":"Mao Kelong","year":"2021","unstructured":"Kelong Mao , Xi Xiao , Tingyang Xu , Yu Rong , Junzhou Huang , and Peilin Zhao . 2021. Molecular graph enhanced transformer for retrosynthesis prediction. Neurocomputing ( 2021 ). Kelong Mao, Xi Xiao, Tingyang Xu, Yu Rong, Junzhou Huang, and Peilin Zhao. 2021. Molecular graph enhanced transformer for retrosynthesis prediction. Neurocomputing (2021)."},{"key":"e_1_3_2_1_27_1","volume-title":"Conditional variational autoencoder for neural machine translation. arXiv preprint arXiv:1812.04405","author":"Pagnoni Artidoro","year":"2018","unstructured":"Artidoro Pagnoni , Kevin Liu , and Shangyan Li. 2018. Conditional variational autoencoder for neural machine translation. arXiv preprint arXiv:1812.04405 ( 2018 ). Artidoro Pagnoni, Kevin Liu, and Shangyan Li. 2018. Conditional variational autoencoder for neural machine translation. arXiv preprint arXiv:1812.04405 (2018)."},{"key":"e_1_3_2_1_28_1","volume-title":"Proc. of ICML.","author":"Rezende Danilo Jimenez","year":"2014","unstructured":"Danilo Jimenez Rezende , Shakir Mohamed , and Daan Wierstra . 2014 . Stochastic Backpropagation and Approximate Inference in Deep Generative Models . In Proc. of ICML. Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra. 2014. Stochastic Backpropagation and Approximate Inference in Deep Generative Models. In Proc. of ICML."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"crossref","unstructured":"David Rogers and Mathew Hahn. 2010. Extended-Connectivity Fingerprints. J. Chem. Inf. Model. (2010).  David Rogers and Mathew Hahn. 2010. Extended-Connectivity Fingerprints. J. Chem. Inf. Model. (2010).","DOI":"10.1021\/ci100050t"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"crossref","unstructured":"Miko\u0140aj Sacha Miko\u0140aj B\u0140a\u012c Piotr Byrski Pawe\u0140 D\u0105browski-Tuma\u0140ski Miko\u0140aj Chromi\u0140ski Rafa\u0140 Loska Pawe\u0140 W\u0140odarczyk-Pruszy\u0140ski and Stanis\u0140aw Jastrz\u0229bski. 2021. Molecule Edit Graph Attention Network: Modeling Chemical Reactions as Sequences of Graph Edits. J. Chem. Inf. Model. (2021).  Miko\u0140aj Sacha Miko\u0140aj B\u0140a\u012c Piotr Byrski Pawe\u0140 D\u0105browski-Tuma\u0140ski Miko\u0140aj Chromi\u0140ski Rafa\u0140 Loska Pawe\u0140 W\u0140odarczyk-Pruszy\u0140ski and Stanis\u0140aw Jastrz\u0229bski. 2021. Molecule Edit Graph Attention Network: Modeling Chemical Reactions as Sequences of Graph Edits. J. Chem. Inf. Model. (2021).","DOI":"10.1021\/acs.jcim.1c00537"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"crossref","unstructured":"Nadine Schneider Nikolaus Stiefl and Gregory A Landrum. 2016. What's what: The (nearly) definitive guide to reaction role assignment. J. Chem. Inf. Model. (2016).  Nadine Schneider Nikolaus Stiefl and Gregory A Landrum. 2016. What's what: The (nearly) definitive guide to reaction role assignment. J. Chem. Inf. Model. (2016).","DOI":"10.1021\/acs.jcim.6b00564"},{"key":"e_1_3_2_1_32_1","volume-title":"Neural-symbolic machine learning for retrosynthesis and reaction prediction. Chemistry--A European Journal","author":"Segler Marwin HS","year":"2017","unstructured":"Marwin HS Segler and Mark P Waller . 2017. Neural-symbolic machine learning for retrosynthesis and reaction prediction. Chemistry--A European Journal ( 2017 ). Marwin HS Segler and Mark P Waller. 2017. Neural-symbolic machine learning for retrosynthesis and reaction prediction. Chemistry--A European Journal (2017)."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i1.16131"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.10983"},{"key":"e_1_3_2_1_35_1","volume-title":"Proc. of ICML.","author":"Shi Chence","year":"2020","unstructured":"Chence Shi , Minkai Xu , Hongyu Guo , Ming Zhang , and Jian Tang . 2020 . A Graph to Graphs Framework for Retrosynthesis Prediction . In Proc. of ICML. Chence Shi, Minkai Xu, Hongyu Guo, Ming Zhang, and Jian Tang. 2020. A Graph to Graphs Framework for Retrosynthesis Prediction. In Proc. of ICML."},{"key":"e_1_3_2_1_36_1","volume-title":"Proc. of NeurIPS.","author":"Sohn Kihyuk","year":"2015","unstructured":"Kihyuk Sohn , Honglak Lee , and Xinchen Yan . 2015 . Learning Structured Output Representation using Deep Conditional Generative Models . In Proc. of NeurIPS. Kihyuk Sohn, Honglak Lee, and Xinchen Yan. 2015. Learning Structured Output Representation using Deep Conditional Generative Models. In Proc. of NeurIPS."},{"key":"e_1_3_2_1_37_1","volume-title":"Learning Graph Models for Template-Free Retrosynthesis. ICML Workshop","author":"Somnath Vignesh Ram","year":"2020","unstructured":"Vignesh Ram Somnath , Charlotte Bunne , Connor W. Coley , Andreas Krause , and Regina Barzilay . 2020 . Learning Graph Models for Template-Free Retrosynthesis. ICML Workshop (2020). Vignesh Ram Somnath, Charlotte Bunne, Connor W. Coley, Andreas Krause, and Regina Barzilay. 2020. Learning Graph Models for Template-Free Retrosynthesis. ICML Workshop (2020)."},{"key":"e_1_3_2_1_38_1","volume-title":"Proc. of NeurIPS.","author":"S\u00f8nderby Casper Kaae","year":"2016","unstructured":"Casper Kaae S\u00f8nderby , Tapani Raiko , Lars Maal\u00f8e , S\u00f8ren Kaae S\u00f8nderby , and Ole Winther . 2016 . Ladder Variational Autoencoders . In Proc. of NeurIPS. Casper Kaae S\u00f8nderby, Tapani Raiko, Lars Maal\u00f8e, S\u00f8ren Kaae S\u00f8nderby, and Ole Winther. 2016. Ladder Variational Autoencoders. In Proc. of NeurIPS."},{"key":"e_1_3_2_1_39_1","volume-title":"Energy-based View of Retrosynthesis. arXiv preprint arXiv:2007.13437","author":"Sun Ruoxi","year":"2020","unstructured":"Ruoxi Sun , Hanjun Dai , Li Li , Steven Kearnes , and Bo Dai . 2020. Energy-based View of Retrosynthesis. arXiv preprint arXiv:2007.13437 ( 2020 ). Ruoxi Sun, Hanjun Dai, Li Li, Steven Kearnes, and Bo Dai. 2020. Energy-based View of Retrosynthesis. arXiv preprint arXiv:2007.13437 (2020)."},{"key":"e_1_3_2_1_40_1","volume-title":"Le","author":"Sutskever Ilya","year":"2014","unstructured":"Ilya Sutskever , Oriol Vinyals , and Quoc V . Le . 2014 . Sequence to Sequence Learning with Neural Networks. In Proc. of NIPS. Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. 2014. Sequence to Sequence Learning with Neural Networks. In Proc. of NIPS."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"crossref","unstructured":"I.V. Tetko P. Karpov and R. Van Deursen. 2020. State-of-the-art augmented NLP transformer models for direct and single-step retro synthesis. Nat. Comm. (2020).  I.V. Tetko P. Karpov and R. Van Deursen. 2020. State-of-the-art augmented NLP transformer models for direct and single-step retro synthesis. Nat. Comm. (2020).","DOI":"10.1038\/s41467-020-19266-y"},{"key":"e_1_3_2_1_42_1","volume-title":"The atom economy--a search for synthetic efficiency. Science","author":"Trost Barry M","year":"1991","unstructured":"Barry M Trost . 1991. The atom economy--a search for synthetic efficiency. Science ( 1991 ). Barry M Trost. 1991. The atom economy--a search for synthetic efficiency. Science (1991)."},{"key":"e_1_3_2_1_43_1","volume-title":"Permutation invariant graph-to-sequence model for template-free retrosynthesis and reaction prediction. arXiv preprint arXiv:2110.09681","author":"Tu Zhengkai","year":"2021","unstructured":"Zhengkai Tu and Connor W Coley . 2021. Permutation invariant graph-to-sequence model for template-free retrosynthesis and reaction prediction. arXiv preprint arXiv:2110.09681 ( 2021 ). Zhengkai Tu and Connor W Coley. 2021. Permutation invariant graph-to-sequence model for template-free retrosynthesis and reaction prediction. arXiv preprint arXiv:2110.09681 (2021)."},{"key":"e_1_3_2_1_44_1","volume-title":"Proc. of NeurIPS.","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani , Noam Shazeer , Niki Parmar , Jakob Uszkoreit , Llion Jones , Aidan N Gomez , \u0141ukasz Kaiser , and Illia Polosukhin . 2017 . Attention is all you need . In Proc. of NeurIPS. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Proc. of NeurIPS."},{"key":"e_1_3_2_1_45_1","volume-title":"A Diverse, plausible and Transformer-based method for Single-Step retrosynthesis predictions. Chemical Engineering Journal","author":"Wang Xiaorui","year":"2021","unstructured":"Xiaorui Wang , Yuquan Li , Jiezhong Qiu , Guangyong Chen , Huanxiang Liu , Benben Liao , Chang-Yu Hsieh , and Xiaojun Yao . 2021. RetroPrime : A Diverse, plausible and Transformer-based method for Single-Step retrosynthesis predictions. Chemical Engineering Journal ( 2021 ). Xiaorui Wang, Yuquan Li, Jiezhong Qiu, Guangyong Chen, Huanxiang Liu, Benben Liao, Chang-Yu Hsieh, and Xiaojun Yao. 2021. RetroPrime: A Diverse, plausible and Transformer-based method for Single-Step retrosynthesis predictions. Chemical Engineering Journal (2021)."},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"crossref","unstructured":"David Weininger. 1988. SMILES a chemical language and information system. 1. Introduction to methodology and encoding rules. J. Chem. Inf. Model. (1988).  David Weininger. 1988. SMILES a chemical language and information system. 1. Introduction to methodology and encoding rules. J. Chem. Inf. Model. (1988).","DOI":"10.1021\/ci00057a005"},{"key":"e_1_3_2_1_47_1","volume-title":"Proc. of NeurIPS.","author":"Yan Chaochao","year":"2020","unstructured":"Chaochao Yan , Qianggang Ding , Peilin Zhao , Shuangjia Zheng , Jinyu Yang , Yang Yu , and Junzhou Huang . 2020 . RetroXpert: Decompose Retrosynthesis Prediction Like A Chemist . In Proc. of NeurIPS. Chaochao Yan, Qianggang Ding, Peilin Zhao, Shuangjia Zheng, Jinyu Yang, Yang Yu, and Junzhou Huang. 2020. RetroXpert: Decompose Retrosynthesis Prediction Like A Chemist. 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Molecular Transformer unifies reaction prediction and retrosynthesis across pharma chemical space. Chemical Communications (2019).  Qingyi Yang Vishnu Sresht Peter Bolgar Xinjun Hou Jacquelyn L Klug-McLeod Christopher R Butler et al. 2019a. Molecular Transformer unifies reaction prediction and retrosynthesis across pharma chemical space. Chemical Communications (2019)."},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i02.5538"},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"crossref","unstructured":"Shuai Yuan Jun-Sheng Qin Jialuo Li Lan Huang Liang Feng Yu Fang Christina Lollar Jiandong Pang Liangliang Zhang Di Sun etal 2018. Retrosynthesis of multi-component metal-organic frameworks. Nat. Comm. (2018).  Shuai Yuan Jun-Sheng Qin Jialuo Li Lan Huang Liang Feng Yu Fang Christina Lollar Jiandong Pang Liangliang Zhang Di Sun et al. 2018. Retrosynthesis of multi-component metal-organic frameworks. Nat. Comm. 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