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We view molecular optimization as a graph-to-graph translation problem.
Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules
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The graph neural network model
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Extended-connectivity fingerprints
David Rogers and Mathew Hahn · 2010
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Matched molecular pairs as a medicinal chemistry tool: miniperspective
Ed Griffen, Andrew G Leach, Graeme R Robb, and Daniel J Warner · 2011
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Quantifying the chemical beauty of drugs
G Richard Bickerton, Gaia V Paolini, Jérémy Besnard, Sorel Muresan, and Andrew L Hopkins · 2012
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Spectral networks and locally connected networks on graphs
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Matched molecular pair analysis in drug discovery
Alexander G Dossetter, Edward J Griffen, and Andrew G Leach · 2013
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Convolutional networks on graphs for learning molecular fingerprints
David K Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarell, Timothy Hirzel, Alán Aspuru-Guzik, and Ryan P Adams · 2015
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Deep convolutional networks on graph-structured data
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Gated graph sequence neural networks
Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel · 2015
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Zinc 15–ligand discovery for everyone
Teague Sterling and John J Irwin · 2015
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Tao Lei, Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2017
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Molecular de-novo design through deep reinforcement learning
Marcus Olivecrona, Thomas Blaschke, Ola Engkvist, and Hongming Chen · 2017
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Schnet: A continuous-filter convolutional neural network for modeling quantum interactions
Kristof Schütt, Pieter-Jan Kindermans, Huziel Enoc Sauceda Felix, Stefan Chmiela, Alexandre Tkatchenko, and Klaus-Robert Müller · 2017
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Style transfer from non-parallel text by cross-alignment
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Molecular graph convolutions: moving beyond fingerprints
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Toward multimodal image-to-image translation
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Syntax-directed variational autoencoder for structured data
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