Fetching the paper…
Reading the bibliography…
Graph diffusion models, dominant in graph generative modeling, remain underexplored for graph-to-graph translation tasks like chemical reaction prediction.
Smiles, a chemical language and information system
David Weininger · 1988
Earlier work this paper cites.
The atom economy—a search for synthetic efficiency
Barry M Trost · 1991
Earlier work this paper cites.
Synthesis of ibuprofen: A greener synthesis of ibuprofen which creates less waste and fewer byproducts
M. C. Cann and M. E. Connelly · 2000
Earlier work this paper cites.
Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions
Peter Ertl and Ansgar Schuffenhauer · 2009
Earlier work this paper cites.
Learning to rank for information retrieval
Tie-Yan Liu et al · 2009
Earlier work this paper cites.
Extraction of Chemical Structures and Reactions from the Literature
Daniel Mark Lowe · 2012
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
Earlier work this paper cites.
Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole · 2016
Earlier work this paper cites.
What’s what: The (nearly) definitive guide to reaction role assignment
Nadine Schneider, Nikolaus Stiefl, and Gregory A Landrum · 2016
Earlier work this paper cites.
Predicting organic reaction outcomes with Weisfeiler-Lehman network
Wengong Jin, Connor Coley, Regina Barzilay, and Tommi Jaakkola · 2017
Earlier work this paper cites.
Neural-symbolic machine learning for retrosynthesis and reaction prediction
Marwin HS Segler and Mark P Waller · 2017
Earlier work this paper cites.
“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
Earlier work this paper cites.
Retrosynthesis prediction with conditional graph logic network
Hanjun Dai, Chengtao Li, Connor Coley, Bo Dai, and Le Song · 2019
Earlier work this paper cites.
Generative models for graph-based protein design
John Ingraham, Vikas Garg, Regina Barzilay, and Tommi Jaakkola · 2019
Earlier work this paper cites.
Predicting retrosynthetic reactions using self-corrected transformer neural networks
Shuangjia Zheng, Jiahua Rao, Zhongyue Zhang, Jun Xu, and Yuedong Yang · 2019
Earlier work this paper cites.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Earlier work this paper cites.
Permutation invariant graph generation via score-based generative modeling
Chenhao Niu, Yang Song, Jiaming Song, Shengjia Zhao, Aditya Grover, and Stefano Ermon · 2020
Earlier work this paper cites.
Temporal graph networks for deep learning on dynamic graphs
Emanuele Rossi, Ben Chamberlain, Fabrizio Frasca, Davide Eynard, Federico Monti, and Michael Bronstein · 2020
Earlier work this paper cites.
On the equivalence between positional node embeddings and structural graph representations
Balasubramaniam Srinivasan and Bruno Ribeiro · 2020
Earlier work this paper cites.
State-of-the-art augmented NLP transformer models for direct and single-step retrosynthesis
Igor V Tetko, Pavel Karpov, Ruud Van Deursen, and Guillaume Godin · 2020
Earlier work this paper cites.
RetroXpert: Decompose retrosynthesis prediction like a chemist
Chaochao Yan, Qianggang Ding, Peilin Zhao, Shuangjia Zheng, JINYU YANG, Yang Yu, and Junzhou Huang · 2020
Earlier work this paper cites.
Structured denoising diffusion models in discrete state-spaces
Jacob Austin, Daniel D. Johnson, Jonathan Ho, Daniel Tarlow, and Rianne van den Berg · 2021
Earlier work this paper cites.
Deep retrosynthetic reaction prediction using local reactivity and global attention
Shuan Chen and Yousung Jung · 2021
Earlier work this paper cites.
Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
Cited alongside, same era.
A generalization of transformer networks to graphs
Vijay Prakash Dwivedi and Xavier Bresson · 2021
Cited alongside, same era.
Argmax flows and multinomial diffusion: Learning categorical distributions
Emiel Hoogeboom, Didrik Nielsen, Priyank Jaini, Patrick Forré, and Max Welling · 2021
Cited alongside, same era.
A survey on knowledge graphs: Representation, acquisition, and applications
Shaoxiong Ji, Shirui Pan, Erik Cambria, Pekka Marttinen, and Philip S. Yu · 2021
Cited alongside, same era.
Valid, plausible, and diverse retrosynthesis using tied two-way transformers with latent variables
Eunji Kim, Dongseon Lee, Youngchun Kwon, Min Sik Park, and Youn-Suk Choi · 2021
Cited alongside, same era.
Molecule edit graph attention network: modeling chemical reactions as sequences of graph edits
Benchmarking graph neural networks
Vijay Prakash Dwivedi, Chaitanya K Joshi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio, and Xavier Bresson · 2023
Later among the works it cites.
User-defined event sampling and uncertainty quantification in diffusion models for physical dynamical systems
Marc Anton Finzi, Anudhyan Boral, Andrew Gordon Wilson, Fei Sha, and Leonardo Zepeda-Nunez · 2023
Later among the works it cites.
Learning chemical rules of retrosynthesis with pre-training
Yinjie Jiang, WEI Ying, Fei Wu, Zhengxing Huang, Kun Kuang, and Zhihua Wang · 2023
Later among the works it cites.
Equivariance with learned canonicalization functions, 2023
Sékou-Oumar Kaba, Arnab Kumar Mondal, Yan Zhang, Yoshua Bengio, and Siamak Ravanbakhsh · 2023
Later among the works it cites.
G2gt: Retrosynthesis prediction with graph-to-graph attention neural network and self-training
Zaiyun Lin, Shiqiu Yin, Lei Shi, Wenbiao Zhou, and Yingsheng John Zhang · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Mikołaj Sacha, Mikołaj Błaz, Piotr Byrski, Paweł Dabrowski-Tumanski, Mikołaj Chrominski, Rafał Loska, Paweł Włodarczyk-Pruszynski, and Stanisław Jastrzebski · 2021
Cited alongside, same era.
GTA: Graph truncated attention for retrosynthesis
Seung-Woo Seo, You Young Song, June Yong Yang, Seohui Bae, Hankook Lee, Jinwoo Shin, Sung Ju Hwang, and Eunho Yang · 2021
Cited alongside, same era.
Finding symmetry breaking order parameters with euclidean neural networks
Tess E. Smidt, Mario Geiger, and Benjamin Kurt Miller · 2021
Cited alongside, same era.
Learning graph models for retrosynthesis prediction
Vignesh Ram Somnath, Charlotte Bunne, Connor Coley, Andreas Krause, and Regina Barzilay · 2021
Cited alongside, same era.
Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2021
Cited alongside, same era.
Towards understanding retrosynthesis by energy-based models
Ruoxi Sun, Hanjun Dai, Li Li, Steven Kearnes, and Bo Dai · 2021
Cited alongside, same era.
Graph-to-graph: Towards accurate and interpretable online handwritten mathematical expression recognition
Jin-Wen Wu, Fei Yin, Yan-Ming Zhang, Xu-Yao Zhang, and Cheng-Lin Liu · 2021
Cited alongside, same era.
FusionRetro: Molecule representation fusion via in-context learning for retrosynthetic planning
Songtao Liu, Zhengkai Tu, Minkai Xu, Zuobai Zhang, Lu Lin, Rex Ying, Jian Tang, Peilin Zhao, and Dinghao Wu · 2023
Later among the works it cites.
Re-evaluating retrosynthesis algorithms with syntheseus
Krzysztof Maziarz, Austin Tripp, Guoqing Liu, Megan Stanley, Shufang Xie, Piotr Gaiński, Philipp Seidl, and Marwin Segler · 2023
Later among the works it cites.
Adversarial diffusion distillation
Axel Sauer, Dominik Lorenz, Andreas Blattmann, and Robin Rombach · 2023
Later among the works it cites.
Parallel sampling of diffusion models
Andy Shih, Suneel Belkhale, Stefano Ermon, Dorsa Sadigh, and Nima Anari · 2023
Later among the works it cites.
AbODE: Ab initio antibody design using conjoined ODEs
Yogesh Verma, Markus Heinonen, and Vikas Garg · 2023
Later among the works it cites.
DiGress: Discrete denoising diffusion for graph generation
Clement Vignac, Igor Krawczuk, Antoine Siraudin, Bohan Wang, Volkan Cevher, and Pascal Frossard · 2023
Later among the works it cites.
RetroDiff: Retrosynthesis as multi-stage distribution interpolation
Yiming Wang, Yuxuan Song, Minkai Xu, Rui Wang, Hao Zhou, and Weiying Ma · 2023
Later among the works it cites.
Practical and asymptotically exact conditional sampling in diffusion models
Luhuan Wu, Brian Trippe, Christian Naesseth, David Blei, and John P Cunningham · 2023
Later among the works it cites.
Retrosynthesis prediction with local template retrieval
Shufang Xie, Rui Yan, Junliang Guo, Yingce Xia, Lijun Wu, and Tao Qin · 2023
Later among the works it cites.
SwinGNN: Rethinking permutation invariance in diffusion models for graph generation
Qi Yan, Zhengyang Liang, Yang Song, Renjie Liao, and Lele Wang · 2023
Later among the works it cites.
Diffusion posterior sampling for linear inverse problem solving: A filtering perspective
Zehao Dou and Yang Song · 2024
Closest in time.
RetroBridge: Modeling retrosynthesis with Markov bridges
Ilia Igashov, Arne Schneuing, Marwin Segler, Michael M Bronstein, and Bruno Correia · 2024
Closest in time.
Improving equivariant networks with probabilistic symmetry breaking
Hannah Lawrence, Vasco Portilheiro, Yan Zhang, and Sékou-Oumar Kaba · 2024
Closest in time.
Discrete diffusion language modeling by estimating the ratios of the data distribution
Aaron Lou, Chenlin Meng, and Stefano Ermon · 2024
Closest in time.
Diffusion twigs with loop guidance for conditional graph generation
Giangiacomo Mercatali, Yogesh Verma, Andre Freitas, and Vikas Garg · 2024
Closest in time.
Improving diffusion models for inverse problems using optimal posterior covariance
Xinyu Peng, Ziyang Zheng, Wenrui Dai, Nuoqian Xiao, Chenglin Li, Junni Zou, and Hongkai Xiong · 2024
Closest in time.
Equivariant symmetry breaking sets, 2024
YuQing Xie and Tess Smidt · 2024
Closest in time.