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We propose a combination of a variational autoencoder and a transformer based model which fully utilises graph convolutional and graph pooling layers to operate directly on graphs.
Iterative partial equalization of orbital electronegativity—a rapid access to atomic charges
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SMILES, a chemical language and information system. 1. introduction to methodology and encoding rules
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Chemical space and biology
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Enumeration of 166 billion organic small molecules in the chemical universe database gdb-17
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Auto-encoding variational bayes
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Quantum chemistry structures and properties of 134 kilo molecules
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Better informed distance geometry: using what we know to improve conformation generation
Riniker, S. and Landrum, G. A · 2015
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Variational graph auto-encoders
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Geometric deep learning: going beyond Euclidean data
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Inductive representation learning on large graphs
Hamilton, W., Ying, Z., and Leskovec, J · 2017
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Semi-supervised classification with graph convolutional networks
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Klein, G., Kim, Y., Deng, Y., Senellart, J., and Rush, A. M · 2017
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Grammar variational autoencoder
Kusner, M. J., Paige, B., and Hernández-Lobato, J. M · 2017
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Automatic chemical design using a data-driven continuous representation of molecules
Gómez-Bombarelli, R., Wei, J. N., Duvenaud, D., Hernández-Lobato, J. M., Sánchez-Lengeling, B., Sheberla, D., Aguilera-Iparraguirre, J., Hirzel, T. D., Adams, R. P., and Aspuru-Guzik, A · 2018
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GraphVAE: Towards generation of small graphs using Variational Autoencoders
Simonovsky, M. and Komodakis, N · 2018
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Graph Attention Networks
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., and Bengio, Y · 2018
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Fast graph representation learning with PyTorch Geometric
Fey, M. and Lenssen, J. E · 2019
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Rdkit open-source cheminformatics toolkit, 2019
Landrum, G · 2019
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Disentangling interpretable generative parameters of random and real-world graphs
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MolGAN: An implicit generative model for small molecular graphs
De Cao, N. and Kipf, T · 2018
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Hierarchical graph representation learning with differentiable pooling
Ying, Z., You, J., Morris, C., Ren, X., Hamilton, W., and Leskovec, J
Cited in the paper.
Hierarchical graph representation learning with differentiable pooling
Ying, Z., You, J., Morris, C., Ren, X., Hamilton, W., and Leskovec, J
Cited in the paper.
Stoehr, N., Brockschmidt, M., Stuehmer, J., and Yilmaz, E · 2019
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Bidirectional molecule generation with recurrent neural networks
Grisoni, F., Moret, M., Lingwood, R., and Schneider, G · 2020
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