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Deep learning models for anticipating the products of organic reactions have found many use cases, including validating retrosynthetic pathways and constraining synthesis-based molecular design tools.
On evaluating adversarial robustness
Nicholas Carlini, Anish Athalye, Nicolas Papernot, Wieland Brendel, Jonas Rauber, Dimitris Tsipras, Ian Goodfellow, Aleksander Madry, and Alexey Kurakin · 1902
Earlier work this paper cites.
HuggingFace’s transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M Rush · 1910
Earlier work this paper cites.
Reaction planning: prediction of new organic reactions
Rainer Herges · 1990
Earlier work this paper cites.
Handwritten digit recognition with a back-propagation network
Yann LeCun, Bernhard E Boser, John S Denker, Donnie Henderson, R E Howard, Wayne E Hubbard, and Lawrence D Jackel · 1990
Earlier work this paper cites.
Palladium-catalyzed aromatic aminations with in situ generated aminostannanes
Anil S Guram and Stephen L Buchwald · 1994
Earlier work this paper cites.
Palladium-catalyzed formation of carbon-nitrogen bonds. reaction intermediates and catalyst improvements in the hetero cross-coupling of aryl halides and tin amides
Frederic Paul, Joe Patt, and John F Hartwig · 1994
Earlier work this paper cites.
Improving predictive inference under covariate shift by weighting the log-likelihood function
Hidetoshi Shimodaira · 2000
Earlier work this paper cites.
SYNOPSIS: SYNthesize and OPtimize system in silico
H Maarten Vinkers, Marc R de Jonge, Frederik F D Daeyaert, Jan Heeres, Lucien M H Koymans, Joop H van Lenthe, Paul J Lewi, Henk Timmerman, Koen Van Aken, and Paul A J Janssen · 2003
Earlier work this paper cites.
Analysis of the reactions used for the preparation of drug candidate molecules
John S Carey, David Laffan, Colin Thomson, and Mike T Williams · 2006
Earlier work this paper cites.
Dataset Shift in Machine Learning
Joaquin Quiñonero-Candela, Masashi Sugiyama, Anton Schwaighofer, and Neil D Lawrence, editors · 2008
Earlier work this paper cites.
ChemBERTa: Large-scale self-supervised pretraining for molecular property prediction
Seyone Chithrananda, Gabriel Grand, and Bharath Ramsundar · 2010
Earlier work this paper cites.
The medicinal chemist’s toolbox: An analysis of reactions used in the pursuit of drug candidates
Stephen D Roughley and Allan M Jordan · 2011
Earlier work this paper cites.
ReactionPredictor: prediction of complex chemical reactions at the mechanistic level using machine learning
Matthew A Kayala and Pierre Baldi · 2012
Earlier work this paper cites.
Extraction of chemical structures and reactions from the literature
Daniel Mark Lowe · 2012
Earlier work this paper cites.
RXNO: reaction ontologies, 2012
RSC · 2012
Earlier work this paper cites.
On causal and anticausal learning
Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, and Joris Mooij · 2012
Earlier work this paper cites.
Machine Learning in Non-Stationary Environments: Introduction to Covariate Shift Adaptation
Masashi Sugiyama and Kawanabe Motoaki · 2012
Earlier work this paper cites.
Time-split cross-validation as a method for estimating the goodness of prospective prediction
Robert P Sheridan · 2013
Earlier work this paper cites.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
Earlier work this paper cites.
A short review of chemical reaction database systems, computer-aided synthesis design, reaction prediction and synthetic feasibility
Wendy A Warr · 2014
Earlier work this paper cites.
SCUBIDOO: A large yet screenable and easily searchable database of computationally created chemical compounds optimized toward high likelihood of synthetic tractability
F Chevillard and P Kolb · 2015
Earlier work this paper cites.
Deep Learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
Earlier work this paper cites.
Big data from pharmaceutical patents: A computational analysis of medicinal chemists’ bread and butter
Nadine Schneider, Daniel M Lowe, Roger A Sayle, Michael A Tarselli, and Gregory A Landrum · 2016
Earlier work this paper cites.
Neural networks for the prediction of organic chemistry reactions
Jennifer N Wei, David Duvenaud, and Alán Aspuru-Guzik · 2016
Earlier work this paper cites.
A baseline for detecting misclassified and out-of-distribution examples in neural networks
Dan Hendrycks and Kevin Gimpel · 2017
Earlier work this paper cites.
Predicting organic reaction outcomes with Weisfeiler-Lehman network
Wengong Jin, Connor W Coley, Regina Barzilay, and Tommi Jaakkola · 2017
Earlier work this paper cites.
Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
Earlier work this paper cites.
Pistachio - search and faceting of large reaction databases
John Mayfield, Daniel Lowe, and Roger Sayle · 2017
Earlier work this paper cites.
Elements of Causal Inference
Jonas Peters, Dominik Janzing, and Bernhard Schölkopf · 2017
Earlier work this paper cites.
Modelling chemical reasoning to predict and invent reactions
Marwin H S Segler and Mark P Waller · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Deep learning for chemical reaction prediction
David Fooshee, Aaron Mood, Eugene Gutman, Mohammadamin Tavakoli, Gregor Urban, Frances Liu, Nancy Huynh, David Van Vranken, and Pierre Baldi · 2018
Earlier work this paper cites.
Reaction prediction and synthesis design
Jonathan M Goodman · 2018
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Dataset bias in the natural sciences: A case study in chemical reaction prediction and synthesis design
Ryan-Rhys Griffiths, Philippe Schwaller, and Alpha Lee · 2018
Cited alongside, same era.
Ray: A distributed framework for emerging AI applications
Philipp Moritz, Robert Nishihara, Stephanie Wang, Alexey Tumanov, Richard Liaw, Eric Liang, Melih Elibol, Zongheng Yang, William Paul, Michael I Jordan, and Ion Stoica · 2018
Cited alongside, same era.
“Found in translation”: Predicting outcomes of complex organic chemistry reactions using neural sequence-to-sequence models
Philippe Schwaller, Theophile Gaudin, David Lanyi, Costas Bekas, and Teodoro Laino · 2018
Cited alongside, same era.
Planning chemical syntheses with deep neural networks and symbolic AI
Marwin H S Segler, Mike Preuss, and Mark P Waller · 2018
Cited alongside, same era.
RDKit: Open-source cheminformatics, 2021
RDKit Team · 2021
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Molecule edit graph attention network: Modeling chemical reactions as sequences of graph edits
Mikołaj Sacha, Mikołaj Błaż, Piotr Byrski, Paweł Dąbrowski-Tumański, Mikołaj Chromiński, Rafał Loska, Paweł Włodarczyk-Pruszyński, and Stanisław Jastrzębski · 2021
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BREEDS: Benchmarks for subpopulation shift
Shibani Santurkar, Dimitris Tsipras, and Aleksander Madry · 2021
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Unassisted noise reduction of chemical reaction data sets
Alessandra Toniato, Philippe Schwaller, Antonio Cardinale, Joppe Geluykens, and Teodoro Laino · 2021
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Learning to split for automatic bias detection
Yujia Bao and Regina Barzilay · 2022
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Amortized tree generation for bottom-up synthesis planning and synthesizable molecular design
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MoleculeNet: a benchmark for molecular machine learning
Zhenqin Wu, Bharath Ramsundar, Evan N Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S Pappu, Karl Leswing, and Vijay Pande · 2018
Cited alongside, same era.
ASKCOS (Automated System for Knowledge-based Continuous Organic Synthesis), 2019
ASKCOS Team · 2019
Cited alongside, same era.
A generative model for electron paths
John Bradshaw, Matt J Kusner, Brooks Paige, Marwin H S Segler, and José Miguel Hernández-Lobato · 2019
Cited alongside, same era.
A model to search for synthesizable molecules
John Bradshaw, Brooks Paige, Matt J Kusner, Marwin H S Segler, and José Miguel Hernández-Lobato · 2019
Cited alongside, same era.
A graph-convolutional neural network model for the prediction of chemical reactivity
Connor W Coley, Wengong Jin, Luke Rogers, Timothy F Jamison, Tommi S Jaakkola, William H Green, Regina Barzilay, and Klavs F Jensen · 2019
Cited alongside, same era.
Graph transformation policy network for chemical reaction prediction
Kien Do, Truyen Tran, and Svetha Venkatesh · 2019
Cited alongside, same era.
The 25th anniversary of the Buchwald–Hartwig amination: Development, applications, and outlook
Paola A Forero-Cortés and Alexander M Haydl · 2019
Cited alongside, same era.
Wenhao Gao, Rocío Mercado, and Connor W Coley · 2022
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LoRA: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2022
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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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NameRxn (expert system for named reaction identification and classification), 2022
NextMove Software · 2022
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Improving few- and zero-shot reaction template prediction using modern Hopfield networks
Philipp Seidl, Philipp Renz, Natalia Dyubankova, Paulo Neves, Jonas Verhoeven, Jörg K Wegner, Marwin Segler, Sepp Hochreiter, and Günter Klambauer · 2022
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Reproducing the invention of a named reaction: zero-shot prediction of unseen chemical reactions
An Su, Xinqiao Wang, Ling Wang, Chengyun Zhang, Yejian Wu, Xinyi Wu, Qingjie Zhao, and Hongliang Duan · 2022
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Permutation Invariant Graph-to-Sequence Model for Template-Free Retrosynthesis and Reaction Prediction
Zhengkai Tu and Connor W Coley · 2022
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ChemistGA: A chemical synthesizable accessible molecular generation algorithm for real-world drug discovery
Jike Wang, Xiaorui Wang, Huiyong Sun, Mingyang Wang, Yundian Zeng, Dejun Jiang, Zhenxing Wu, Zeyi Liu, Ben Liao, Xiaojun Yao, Chang-Yu Hsieh, Dongsheng Cao, Xi Chen, and Tingjun Hou · 2022
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From theory to experiment: transformer-based generation enables rapid discovery of novel reactions
Xinqiao Wang, Chuansheng Yao, Yun Zhang, Jiahui Yu, Haoran Qiao, Chengyun Zhang, Yejian Wu, Renren Bai, and Hongliang Duan · 2022
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OpenOOD: Benchmarking generalized out-of-distribution detection
Jingkang Yang, Pengyun Wang, Dejian Zou, Zitang Zhou, Kunyuan Ding, Wenxuan Peng, Haoqi Wang, Guangyao Chen, Bo Li, Yiyou Sun, Xuefeng Du, Kaiyang Zhou, Wayne Zhang, Dan Hendrycks, Yixuan Li, and Ziwei Liu · 2022
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Reagent prediction with a molecular transformer improves reaction data quality
Mikhail Andronov, Varvara Voinarovska, Natalia Andronova, Michael Wand, Djork-Arné Clevert, and Jürgen Schmidhuber · 2023
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Holistic chemical evaluation reveals pitfalls in reaction prediction models
Victor Sabanza Gil, Andrés M Bran, Malte Franke, Remi Schlama, J Luterbacher, and Philippe Schwaller · 2023
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Transformer performance for chemical reactions: Analysis of different predictive and evaluation scenarios
Fernando Jaume-Santero, Alban Bornet, Alain Valery, Nona Naderi, David Vicente Alvarez, Dimitrios Proios, Anthony Yazdani, Colin Bournez, Thomas Fessard, and Douglas Teodoro · 2023
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Latent biases in machine learning models for predicting binding affinities using popular data sets
Ganesh Chandan Kanakala, Rishal Aggarwal, Divya Nayar, and U Deva Priyakumar · 2023
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SIMPD: An algorithm for generating simulated time splits for validating machine learning approaches
Gregory A Landrum, Maximilian Beckers, Jessica Lanini, Nadine Schneider, Nikolaus Stiefl, and Sereina Riniker · 2023
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Doubly stochastic graph-based non-autoregressive reaction prediction
Ziqiao Meng, Peilin Zhao, Yang Yu, and Irwin King · 2023
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Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava and others · 2023
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Lo-Hi: Practical ML drug discovery benchmark
Simon Steshin · 2023
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Fast customization of chemical language models to out-of-distribution data sets
Alessandra Toniato, Alain C Vaucher, Marzena Maria Lehmann, Torsten Luksch, Philippe Schwaller, Marco Stenta, and Teodoro Laino · 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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Assessing the extrapolation capability of template-free retrosynthesis models
Shuan Chen and Yousung Jung · 2024
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Ideation and evaluation of novel multicomponent reactions via mechanistic network analysis and automation
Babak Mahjour, Juncheng Lu, Jenna Fromer, Nicholas Casetti, and Connor Coley · 2024
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Generative AI for designing and validating easily synthesizable and structurally novel antibiotics
Kyle Swanson, Gary Liu, Denise B Catacutan, Autumn Arnold, James Zou, and Jonathan M Stokes · 2024
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Real-world molecular out-of-distribution: Specification and investigation
Prudencio Tossou, Cas Wognum, Michael Craig, Hadrien Mary, and Emmanuel Noutahi · 2024
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ORDerly: Data sets and benchmarks for chemical reaction data
Daniel S Wigh, Joe Arrowsmith, Alexander Pomberger, Kobi C Felton, and Alexei A Lapkin · 2024
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RetroOOD: Understanding out-of-distribution generalization in retrosynthesis prediction
Yemin Yu, Luotian Yuan, Ying Wei, Hanyu Gao, Xinhai Ye, Zhihua Wang, and Fei Wu · 2024
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