Fetching the paper…
Reading the bibliography…
The central challenge in automated synthesis planning is to be able to generate and predict outcomes of a diverse set of chemical reactions.
Chen, B.; Shen, T.; Jaakkola, T. S.; Barzilay, R. Learning to Make Generalizable and Diverse Predictions for Retrosynthesis. arXiv e-prints 2019
1910
Earlier work this paper cites.
Corey, E. J.; Wipke, W. T. Computer-Assisted Design of Complex Organic Syntheses. Science 1969
1969
Earlier work this paper cites.
Corey, E. J.; Wipke, W. T. Computer-Assisted Design of Complex Organic Syntheses. Science 1969
1969
Earlier work this paper cites.
Weininger, D. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. Journal of Chemical Information and Computer Sciences 1988
1988
Earlier work this paper cites.
Satoh, H.; Funatsu, K. SOPHIA, a Knowledge Base-Guided Reaction Prediction System - Utilization of a Knowledge Base Derived from a Reaction Database. Journal of Chemical Information and Computer Sciences 1995
1995
Earlier work this paper cites.
Todd, M. H. Computer-aided organic synthesis. Chem. Soc. Rev. 2005
2005
Earlier work this paper cites.
2006
Earlier work this paper cites.
Warren, S. Organic synthesis: the disconnection approach ; John Wiley & Sons, 2007
2007
Earlier work this paper cites.
Lowe, D. Extraction of chemical structures and reactions from the literature. Ph.D. thesis, University of Cambridge, 2012
2012
Earlier work this paper cites.
2012
Earlier work this paper cites.
Zimmerman, P. M. Automated discovery of chemically reasonable elementary reaction steps. Journal of Computational Chemistry 2013
2013
Earlier work this paper cites.
2015
Earlier work this paper cites.
Wei, J. N.; Duvenaud, D.; Aspuru-Guzik, A. Neural Networks for the Prediction of Organic Chemistry Reactions. ACS Central Science 2016
2016
Earlier work this paper cites.
Schneider, N.; Stiefl, N.; Landrum, G. A. What’s What: The (Nearly) Definitive Guide to Reaction Role Assignment. Journal of Chemical Information and Modeling 2016
2016
Earlier work this paper cites.
Segler, M. H. S.; Waller, M. P. Modelling Chemical Reasoning to Predict and Invent Reactions. Chemistry – A European Journal 2017
2017
Earlier work this paper cites.
Coley, C. W.; Barzilay, R.; Jaakkola, T. S.; Green, W. H.; Jensen, K. F. Prediction of Organic Reaction Outcomes Using Machine Learning. ACS Central Science 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
Coley, C. W.; Rogers, L.; Green, W. H.; Jensen, K. F. Computer-Assisted Retrosynthesis Based on Molecular Similarity. ACS Central Science 2017
2017
Earlier work this paper cites.
Liu, B.; Ramsundar, B.; Kawthekar, P.; Shi, J.; Gomes, J.; Nguyen, Q. L.; Ho, S.; Sloane, J.; Wender, P.; Pande, V. Retrosynthetic reaction prediction using neural sequence-to-sequence models. ACS central science 2017
2017
Cited alongside, same era.
2017
Cited alongside, same era.
2017
Cited alongside, same era.
2017
Cited alongside, same era.
Schwaller, P.; Laino, T.; Gaudin, T.; Bolgar, P.; Hunter, C. A.; Bekas, C.; Lee, A. A. Molecular Transformer: A Model for Uncertainty-Calibrated Chemical Reaction Prediction. ACS Central Science 2019
2019
Later among the works it cites.
Karpov, P.; Godin, G.; Tetko, I. A Transformer Model for Retrosynthesis ; 2019; pp 817–830
2019
Later among the works it cites.
Do, K.; Tran, T.; Venkatesh, S. Graph Transformation Policy Network for Chemical Reaction Prediction. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. New York, NY, USA, 2019; p 750–760
2019
Later among the works it cites.
Gu, J.; Liu, Q.; Cho, K. Insertion-based Decoding with Automatically Inferred Generation Order. Transactions of the Association for Computational Linguistics 2019
2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2017
Cited alongside, same era.
Blakemore, D. C.; Castro, L.; Churcher, I.; Rees, D. C.; Thomas, A. W.; Wilson, D. M.; Wood, A. Organic synthesis provides opportunities to transform drug discovery. Nature Chemistry 2018
2018
Cited alongside, same era.
Segler, M. H. S.; Preuss, M.; Waller, M. P. Planning chemical syntheses with deep neural networks and symbolic AI. Nature 2018
2018
Cited alongside, same era.
Coley, C. W.; Green, W. H.; Jensen, K. F. Machine Learning in Computer-Aided Synthesis Planning. Accounts of Chemical Research 2018
2018
Cited alongside, same era.
Grzybowski, B.; Szymkuć, S.; Gajewska, E. P.; Molga, K.; Dittwald, P.; Wołoś, A.; Klucznik, T. Chematica: A Story of Computer Code That Started to Think like a Chemist. Chem 2018
2018
Cited alongside, same era.
He, X.; Haffari, G.; Norouzi, M. Sequence to Sequence Mixture Model for Diverse Machine Translation. Proceedings of the 22nd Conference on Computational Natural Language Learning. Brussels, Belgium, 2018; pp 583–592
2018
Cited alongside, same era.
Jiang, S.; de Rijke, M. Why are Sequence-to-Sequence Models So Dull? Understanding the Low-Diversity Problem of Chatbots. Proceedings of the 2018 EMNLP Workshop SCAI: The 2nd International Workshop on Search-Oriented Conversational AI. Brussels, Belgium, 2018; pp 81–86
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Molga, K.; Gajewska, E. P.; Szymkuć, S.; Grzybowski, B. A. The logic of translating chemical knowledge into machine-processable forms: a modern playground for physical-organic chemistry. React. Chem. Eng. 2019
2019
Later among the works it cites.
Gao, W.; Coley, C. W. The Synthesizability of Molecules Proposed by Generative Models. Journal of Chemical Information and Modeling 2020
2020
Closest in time.
Struble, T. J. et al. Current and Future Roles of Artificial Intelligence in Medicinal Chemistry Synthesis. Journal of Medicinal Chemistry 2020
2020
Closest in time.
Roberts, N.; Liang, D.; Neubig, G.; Lipton, Z. C. Decoding and Diversity in Machine Translation. 2020
2020
Closest in time.
Shi, C.; Xu, M.; Guo, H.; Zhang, M.; Tang, J. A Graph to Graphs Framework for Retrosynthesis Prediction. 2020
2020
Closest in time.
Yan, C.; Ding, Q.; Zhao, P.; Zheng, S.; Yang, J.; Yu, Y.; Huang, J. RetroXpert: Decompose Retrosynthesis Prediction like A Chemist. 2020; https://chemrxiv.org/articles/preprint/Interpretable_Retrosynthesis_Prediction_in_Two_Steps/11869692/3
2020
Closest in time.
Gao, W.; Coley, C. W. The Synthesizability of Molecules Proposed by Generative Models. 2020
2020
Closest in time.
2020
Closest in time.
Gottipati, S. K.; Sattarov, B.; Niu, S.; Pathak, Y.; Wei, H.; Liu, S.; Liu, S.; Blackburn, S.; Thomas, K.; Coley, C.; Tang, J.; Chandar, S.; Bengio, Y. Learning to Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning. Proceedings of the 37th International Conference on Machine Learning. 2020; pp 3668–3679
2020
Closest in time.
Wang, X.; Qiu, J.; Li, Y.; Chen, G.; Liu, H.; Liao, B.; Hsieh, C.-Y.; Yao, X. RetroPrime: A Chemistry-Inspired and Transformer-based Method for Retrosynthesis Predictions. 2020; https://chemrxiv.org/articles/preprint/RetroPrime_A_Chemistry-Inspired_and_Transformer-based_Method_for_Retrosynthesis_Predictions/12971942/1
2020
Closest in time.
Thakkar, A.; Chadimová, V.; Bjerrum, E. J.; Engkvist, O.; Reymond, J.-L. Retrosynthetic accessibility score (RAscore) – rapid machine learned synthesizability classification from AI driven retrosynthetic planning. Chem. Sci. 2021
2021
Closest in time.
Tetko, I. V.; Karpov, P.; Van Deursen, R.; Godin, G. State-of-the-art augmented NLP transformer models for direct and single-step retrosynthesis. Nature Communications 2020
2041
Closest in time.
Salatin, T. D.; Jorgensen, W. L. Computer-assisted mechanistic evaluation of organic reactions. 1. Overview. The Journal of Organic Chemistry 1980
2051
Closest in time.