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Learning the underlying distribution of molecular graphs and generating high-fidelity samples is a fundamental research problem in drug discovery and material science.
On the evolution of random graphs
Paul Erdős, Alfréd Rényi, et al · 1960
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Brenda, the enzyme database: updates and major new developments
Ida Schomburg, Antje Chang, Christian Ebeling, Marion Gremse, Christian Heldt, Gregor Huhn, and Dietmar Schomburg · 2004
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Explicit exponential runge-kutta methods for semilinear parabolic problems
Marlis Hochbruck and Alexander Ostermann · 2005
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Collective classification in network data
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Exponential integrators
Marlis Hochbruck and Alexander Ostermann · 2010
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Extended-connectivity fingerprints
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ZINC: A free tool to discover chemistry for biology
John J. Irwin, Teague Sterling, Michael M. Mysinger, Erin S. Bolstad, and Ryan G. Coleman · 2012
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Quantum chemistry structures and properties of 134 kilo molecules
Raghunathan Ramakrishnan, Pavlo O Dral, Matthias Rupp, and O Anatole von Lilienfeld · 2014
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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Rdkit: Open-source cheminformatics software
Greg Landrum · 2016
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Variational graph auto-encoders
Thomas N Kipf and Max Welling · 2016
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Grammar variational autoencoder
Matt J. Kusner, Brooks Paige, and José Miguel Hernández-Lobato · 2017
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Junction tree variational autoencoder for molecular graph generation
Wengong Jin, Regina Barzilay, and Tommi S. Jaakkola · 2018
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Automatic chemical design using a data-driven continuous representation of molecules
Rafael Gómez-Bombarelli, Jennifer N Wei, David Duvenaud, José Miguel Hernández-Lobato, Benjamín Sánchez-Lengeling, Dennis Sheberla, Jorge Aguilera-Iparraguirre, Timothy D Hirzel, Ryan P Adams, and Alán Aspuru-Guzik · 2018
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Syntax-directed variational autoencoder for structured data
Hanjun Dai, Yingtao Tian, Bo Dai, Steven Skiena, and Le Song · 2018
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Graphvae: Towards generation of small graphs using variational autoencoders
Martin Simonovsky and Nikos Komodakis · 2018
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Constrained graph variational autoencoders for molecule design
Qi Liu, Miltiadis Allamanis, Marc Brockschmidt, and Alexander L. Gaunt · 2018
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Molgan: An implicit generative model for small molecular graphs
Nicola De Cao and Thomas Kipf · 2018
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Defactor: Differentiable edge factorization-based probabilistic graph generation
Rim Assouel, Mohamed Ahmed, Marwin H. S. Segler, Amir Saffari, and Yoshua Bengio · 2018
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Fréchet chemnet distance: A metric for generative models for molecules in drug discovery
Kristina Preuer, Philipp Renz, Thomas Unterthiner, Sepp Hochreiter, and Günter Klambauer · 2018
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Neural ordinary differential equations
Ricky T. Q. Chen, Yulia Rubanova, Jesse Bettencourt, and David Duvenaud · 2018
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Modeling relational data with graph convolutional networks
Michael Sejr Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, and Max Welling · 2018
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Variational diffusion models
Diederik Kingma, Tim Salimans, Ben Poole, and Jonathan Ho · 2021
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Argmax flows and multinomial diffusion: Learning categorical distributions
Emiel Hoogeboom, Didrik Nielsen, Priyank Jaini, Patrick Forré, and Max Welling · 2021
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Structured denoising diffusion models in discrete state-spaces
Jacob Austin, Daniel D. Johnson, Jonathan Ho, Daniel Tarlow, and Rianne van den Berg · 2021
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Do transformers really perform badly for graph representation?
Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, and Tie-Yan Liu · 2021
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Rethinking graph transformers with spectral attention
Devin Kreuzer, Dominique Beaini, Will Hamilton, Vincent Létourneau, and Prudencio Tossou · 2021
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Score-based generative modeling of graphs via the system of stochastic differential equations
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Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
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Efficient graph generation with graph recurrent attention networks
Renjie Liao, Yujia Li, Yang Song, Shenlong Wang, William L. Hamilton, David Duvenaud, Raquel Urtasun, and Richard S. Zemel · 2019
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Graphaf: a flow-based autoregressive model for molecular graph generation
Chence Shi, Minkai Xu, Zhaocheng Zhu, Weinan Zhang, Ming Zhang, and Jian Tang · 2020
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Moflow: an invertible flow model for generating molecular graphs
Chengxi Zang and Fei Wang · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Strategies for pre-training graph neural networks
Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay S. Pande, and Jure Leskovec · 2020
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A generalization of transformer networks to graphs
Vijay Prakash Dwivedi and Xavier Bresson · 2020
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Jaehyeong Jo, Seul Lee, and Sung Ju Hwang · 2022
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Fast sampling of diffusion models with exponential integrator
Qinsheng Zhang and Yongxin Chen · 2022
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Dpm-solver: A fast ODE solver for diffusion probabilistic model sampling in around 10 steps
Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu · 2022
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Spanning tree-based graph generation for molecules
Sungsoo Ahn, Binghong Chen, Tianzhe Wang, and Le Song · 2022
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Geodiff: A geometric diffusion model for molecular conformation generation
Minkai Xu, Lantao Yu, Yang Song, Chence Shi, Stefano Ermon, and Jian Tang · 2022
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Torsional diffusion for molecular conformer generation
Bowen Jing, Gabriele Corso, Jeffrey Chang, Regina Barzilay, and Tommi S. Jaakkola · 2022
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Equivariant diffusion for molecule generation in 3d
Emiel Hoogeboom, Victor Garcia Satorras, Clément Vignac, and Max Welling · 2022
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Pseudo numerical methods for diffusion models on manifolds
Luping Liu, Yi Ren, Zhijie Lin, and Zhou Zhao · 2022
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Top-n: Equivariant set and graph generation without exchangeability
Clement Vignac and Pascal Frossard · 2022
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Evaluation metrics for graph generative models: Problems, pitfalls, and practical solutions
Leslie O’Bray, Max Horn, Bastian Rieck, and Karsten Borgwardt · 2022
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On evaluation metrics for graph generative models
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Graphgdp: Generative diffusion processes for permutation invariant graph generation
Han Huang, Leilei Sun, Bowen Du, Yanjie Fu, and Weifeng Lv · 2022
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