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Recent work in graph neural networks (GNNs) has led to improvements in molecular activity and property prediction tasks.
Graph Convolutional Networks with EigenPooling
Yao Ma, Suhang Wang, Charu C. Aggarwal, and Jiliang Tang · 1904
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Toxicity testing in the 21st century: a vision and a strategy
National Research Council et al · 2007
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A Tutorial on Spectral Clustering
Ulrike von Luxburg · 2007
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Virtual high throughput screening (vhts)-a perspective
Sangeetha Subramaniam, Monica Mehrotra, and Dinesh Gupta · 2008
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Learning Thin Junction Trees via Graph Cuts
Shahaf Dafna and Carlos Guestrin · 2009
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Extended-connectivity fingerprints
David Rogers and Mathew Hahn · 2010
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Fragmentation methods: A route to accurate calculations on large systems
Mark S Gordon, Dmitri G Fedorov, Spencer R Pruitt, and Lyudmila V Slipchenko · 2011
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David I Shuman, Sunil K Narang, Pascal Frossard, Antonio Ortega, and Pierre Vandergheynst · 2012
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Alex Graves, Greg Wayne, and Ivo Danihelka · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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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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Discrete Signal Processing on Graphs: Sampling Theory
Siheng Chen, Rohan Varma, Aliaksei Sandryhaila, and Jelena Kovačević · 2015
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Deep Convolutional Networks on Graph-Structured Data
Mikael Henaff, Joan Bruna, and Yann LeCun · 2015
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Gated graph sequence neural networks
Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel · 2015
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Get your atoms in order - an open-source implementation of a novel and robust molecular canonicalization algorithm
Nadine Schneider, Roger A Sayle, and Gregory A Landrum · 2015
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Convolutional neural networks on graphs with fast localized spectral filtering
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst · 2016
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Molecular graph convolutions: moving beyond fingerprints
Steven Kearnes, Kevin McCloskey, Marc Berndl, Vijay Pande, and Patrick Riley · 2016
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On Controllable Sparse Alternatives to Softmax
Anirban Laha, Saneem Ahmed Chemmengath, Priyanka Agrawal, Mitesh Khapra, Karthik Sankaranarayanan, and Harish G Ramaswamy · 2016
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MolGAN: An implicit generative model for small molecular graphs
Nicola De Cao and Thomas Kipf · 2018
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Graph U-Net
Hongyang Gao and Shuiwang Ji · 2018
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Junction Tree Variational Autoencoder for Molecular Graph Generation
Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2018
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Explanation in artificial intelligence: Insights from the social sciences
Tim Miller · 2018
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GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders
Martin Simonovsky and Nikos Komodakis · 2018
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Moleculenet: a benchmark for molecular machine learning
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André F. T. Martins and Ramón Fernandez Astudillo · 2016
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Neural Message Passing for Quantum Chemistry
Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl · 2017
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drugan: an advanced generative adversarial autoencoder model for de novo generation of new molecules with desired molecular properties in silico
Artur Kadurin, Sergey Nikolenko, Kuzma Khrabrov, Alex Aliper, and Alex Zhavoronkov · 2017
Cited alongside, same era.
Grammar variational autoencoder
Matt J Kusner, Brooks Paige, and José Miguel Hernández-Lobato · 2017
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Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
Cited alongside, same era.
Ilya Tolstikhin, Olivier Bousquet, Sylvain Gelly, and Bernhard Schoelkopf · 2017
Cited alongside, same era.
DEFactor: Differentiable Edge Factorization-based Probabilistic Graph Generation
Rim Assouel, Mohamed Ahmed, Marwin H. Segler, Amir Saffari, and Yoshua Bengio · 2018
Cited alongside, same era.
Zhenqin Wu, Bharath Ramsundar, Evan N Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S Pappu, Karl Leswing, and Vijay Pande · 2018
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How Powerful are Graph Neural Networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2018
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Hierarchical Graph Representation Learning with Differentiable Pooling
Rex Ying, Jiaxuan You, Christopher Morris, Xiang Ren, William L. Hamilton, and Jure Leskovec · 2018
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Graphrnn: Generating realistic graphs with deep auto-regressive models
Jiaxuan You, Rex Ying, Xiang Ren, William L Hamilton, and Jure Leskovec · 2018
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An end-to-end deep learning architecture for graph classification
Muhan Zhang, Zhicheng Cui, Marion Neumann, and Yixin Chen · 2018
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Explainability methods for graph convolutional neural networks
Phillip E Pope, Soheil Kolouri, Mohammad Rostami, Charles E Martin, and Heiko Hoffmann · 2019
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A comprehensive survey on graph neural networks
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and Philip S Yu · 2019
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