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Graphs are ubiquitous data structures for representing interactions between entities.
Ueber isomere dehydrocamphersäuren, lauronolsäuren und bihydrolauro-lactone
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Estimation and prediction for stochastic blockmodels for graphs with latent block structure
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The harvard clean energy project: large-scale computational screening and design of organic photovoltaics on the world community grid
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Quantum chemistry structures and properties of 134 kilo molecules
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Convolutional neural networks on graphs with fast localized spectral filtering
M. Defferrard, X. Bresson, and P. Vandergheynst · 2016
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Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach
R. Gómez-Bombarelli, J. Aguilera-Iparraguirre, T. D. Hirzel, D. Duvenaud, D. Maclaurin, M. A. Blood-Forsythe, H. S. Chae, M. Einzinger, D.-G. Ha, T. Wu, et al · 2016
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Gated graph sequence neural networks
Y. Li, D. Tarlow, M. Brockschmidt, and R. Zemel · 2016
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Order matters: Sequence to sequence for sets
O. Vinyals, S. Bengio, and M. Kudlur · 2016
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Generating focused molecule libraries for drug discovery with recurrent neural networks
M. H. Segler, T. Kogej, C. Tyrchan, and M. P. Waller · 2017
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Tackling over-pruning in variational autoencoders
S. Yeung, A. Kannan, Y. Dauphin, and L. Fei-Fei · 2017
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Learning to represent programs with graphs
M. Allamanis, M. Brockschmidt, and M. Khademi · 2018
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Automatic chemical design using a data-driven continuous representation of molecules
R. Gómez-Bombarelli, D. K. Duvenaud, J. M. Hernández-Lobato, J. Aguilera-Iparraguirre, T. D. Hirzel, R. P. Adams, and A. Aspuru-Guzik · 2018
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Junction tree variational autoencoder for molecular graph generation
W. Jin, R. Barzilay, and T. Jaakkola · 2018
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J. Gilmer, S. S. Schoenholz, P. F. Riley, O. Vinyals, and G. E. Dahl · 2017
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Learning graphical state transitions
D. D. Johnson · 2017
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Semi-supervised classification with graph convolutional networks
T. N. Kipf and M. Welling · 2017
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Grammar variational autoencoder
M. J. Kusner, B. Paige, and J. M. Hernández-Lobato · 2017
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Molecular de-novo design through deep reinforcement learning
M. Olivecrona, T. Blaschke, O. Engkvist, and H. Chen · 2017
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3D graph neural networks for RGBD semantic segmentation
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The Harvard clean energy project database http://cepdb.molecularspace.org
J. Hachmann, C. Román-Salgado, K. Trepte, A. Gold-Parker, M. Blood-Forsythe, L. Seress, R. Olivares-Amaya, and A. Aspuru-Guzik
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T. Kipf, E. Fetaya, K.-C. Wang, M. Welling, and R. Zemel · 2018
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Learning deep generative models of graphs
Y. Li, O. Vinyals, C. Dyer, R. Pascanu, and P. Battaglia · 2018
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Designing random graph models using variational autoencoders with applications to chemical design
B. Samanta, A. De, N. Ganguly, and M. Gomez-Rodriguez · 2018
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Towards variational generation of small graphs
M. Simonovsky and N. Komodakis · 2018
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Graphrnn: A deep generative model for graphs
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