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A graph neural network (GNN) is a good choice for predicting the chemical properties of molecules.
Structural determination of paraffin boiling points
Harry Wiener · 1947
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A Reduction of a Graph to a Canonical Form and an Algebra Arising during this Reduction
b. Weisfeiler and A. A. Lehman · 1968
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Random Forests
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Fast Subtree Kernels on Graphs
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Molecular descriptors for chemoinformatics: volume I: alphabetical listing/volume II: appendices, references , volume 41
Roberto Todeschini and Viviana Consonni · 2009
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Extended-connectivity fingerprints
David Rogers and Mathew Hahn · 2010
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Weisfeiler-Lehman Graph Kernels
Nino Shervashidze, Pascal Schweitzer, Erick Jan van Leeuwen, Kurt Mehlhorn, and Karsten M Borgwardt · 2011
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ImageNet Classification with Deep Convolutional Neural Networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Enumeration of 166 billion organic small molecules in the chemical universe database GDB-17
Lars Ruddigkeit, Ruud van Deursen, Lorenz C. Blum, and Reymond Jean-Louis · 2012
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Representation learning: A review and new perspectives
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Distributed Representations of Words and Phrases and their Compositionality
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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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Convolutional Networks on Graphs for Learning Molecular Fingerprints
David Duvenaud, Dougal Maclaurin, Jorge Aguilera-Iparraguirre, Rafael Gómez-Bombarelli, Timothy Hirzel, Alán Aspuru-Guzik, and Ryan P. Adams · 2015
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Adam: a Method for Stochastic Optimization
Diederik P. Kingma and Jimmy Lei Ba · 2015
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Deep Residual Learning for Image Recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Gated Graph Sequence Neural Networks
Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel · 2016
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Understanding deep learning requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 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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Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network
Wengong Jin, Connor W. Coley, Regina Barzilay, and Tommi Jaakkola · 2017
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Semi-supervised Classification with Graph Convolutional Networks
Thomas N. Kipf and Max Welling · 2017
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Deriving neural architectures from sequence and graph kernels
Tao Lei, Wengong Jin, Regina Barzily, and Tommi Jaakkola · 2017
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Graph Classification via Deep Learning with Virtual Nodes
Trang Pham, Truyen Tran, Hoa Dam, and Svetha Venkatesh · 2017
A Generative Model for Electron Paths
John Bradshaw, Matt J. Kusner, Brooks Paige, Marwin H. S. Segler, and José Miguel Hernández-lobato · 2019
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Relational graph attention networks
Dan Busbridge, Dane Sherburn, Pietro Cavallo, and Nils Y Hammerla · 2019
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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
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Graph Warp Module: an Auxiliary Module for Boosting the Power of Graph Neural Networks in Molecular Graph Analysis
Katsuhiko Ishiguro, Shinichi Maeda, and Masanori Koyama · 2019
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Learning multimodal graph-to-graph translation for molecular optimizatoin
Wengong Jin, Kevin Yang, Regina Barzilay, and Tommi Jaakkola · 2019
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Modeling Relational Data with Graph Convolutional Networks
Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, and Max Welling · 2017
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The implicit bias of gradient descent on separable data
Daniel Soudry, Elad Hoffer, Mor Shpigel Nacson, Suriya Gunasekar, and Nathan Srebro · 2017
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MolGAN: An implicit generative model for small molecular graphs
Nicola De Cao and Thomas Kipf · 2018
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Junction tree variational autoencoder for molecular graph generation
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Deeper Insights into Graph Convolutional Networks for Semi-supervised Learning
Qimai Li, Zhichao Han, and Xiao-ming Wu · 2018
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Foundations of machine learning
Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar · 2018
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DeepGCNs: Can GCNs go as deep as CNNs?
Guohao Li, Matthias Muller, Ali Thabet, and Bernard Ghanem · 2019
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N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules
Shengchao Liu, Mehmet Furkan Demirel, and Yingyu Liang Liang · 2019
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Weisfeiler and Leman Go Neural: Higher-Order Graph Neural Networks
Christopher Morris, Martin Ritzert, Matthias Fey, William L. Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe · 2019
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Revisiting Graph Neural Networks: All We Have is Low-Pass Filters
Hoang NT and Takanori Maehara · 2019
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Approximation ratios of graph neural networks for combinatorial problems
Ryoma Sato, Makoto Yamada, and Hisashi Kashima · 2019
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TeaNet: Universal Neural Network Interatomic Potential Inspired by Iterative Electronic Relaxations
So Takamoto and Satoshi Izumi · 2019
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How Powerful are Graph Neural Networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
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Are Learned Molecular Representations Ready For Prime Time?
Kevin Yang, Kyle Swanson, Wengong Jin, Connor Coley, Philipp Eiden, Hua Gao, Angel Guzman-Perez, Timothy Hopper, Brian Kelley, Miriam Mathea, Andrew Palmer, Volker Settels, Tommi Jaakkola, Klavs Jensen, and Regina Barzilay · 2019
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Strategies for pre-training graph neural neworks
Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, and Jure Leskovec · 2020
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Directional Message Passing For Molecular Graphs
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Graph neural networks exponentially lose expressive power for node classification
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March’s advanced organic chemistry: reactions, mechanisms, and structure
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PairNorm: Tackling Oversmoothing in GNNs
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