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Molecular optimization aims to discover novel molecules with desirable properties.
On Bayesian methods for seeking the extremum
J. Močkus · 1975
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SMILES, a chemical language and information system. 1. introduction to methodology and encoding rules
David Weininger · 1988
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Bayesian grammar induction for language modeling
Stanley F. Chen · 1995
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Hyperedge replacement graph grammars , volume 1, chapter 2, pages 95–162
Frank Drewes, H-J. Kreowski, and Annegret Habel · 1997
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MDL-based context-free graph grammar induction and applications
Istvan Jonyer, Lawrence B. Holder, and Diane J. Cook · 2004
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Efficient drug lead discovery and optimization
William L Jorgensen · 2009
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ZINC: A free tool to discover chemistry for biology
John J. Irwin, Teague Sterling, Michael M. Mysinger, Erin S. Bolstad, and Ryan G. Coleman · 2012
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Learning phrase representations using RNN encoder–decoder for statistical machine translation
Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio · 2014
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Auto-encoding variational bayes
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Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach
Rafael Gómez-Bombarelli, Jorge Aguilera-Iparraguirre, Timothy D. Hirzel, David Duvenaud, Dougal Maclaurin, Martin A. Blood-Forsythe, Hyun Sik Chae, Markus Einzinger, Dong-Gwang Ha, Tony Wu, Georgios Markopoulos, Soonok Jeon, Hosuk Kang, Hiroshi Miyazaki, Masaki Numata, Sunghan Kim, Wenliang Huang, Seong Ik Hong, Marc Baldo, Ryan P. Adams, and Alan Aspuru-Guzik · 2016
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β \beta -VAE: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 2017
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Grammar variational autoencoder
Matt J. Kusner, Brooks Paige, and José Miguel Hernández-Lobato · 2017
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Syntax-directed variational autoencoder for structured data
Hanjun Dai, Yingtao Tian, Bo Dai, Steven Skiena, and Le Song · 2018
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Automatic chemical design using a data-driven continuous representation of molecules
Rafael Gómez-Bombarelli, Jennifer N. Wei, David Duvenaud, José Miguel Hernández-Lobato, Benjamín Sánchez-Lengeling, Dennis Sheberla, Jorge Aguilera-Iparraguirre, Timothy D. Hirzel, Ryan P. Adams, and Alán Aspuru-Guzik · 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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GPyOpt: A Bayesian optimization framework in Python
The GPyOpt authors · 2016
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Objective-reinforced generative adversarial networks (ORGAN) for sequence generation models
Gabriel Lima Guimaraes, Benjamin Sanchez-Lengeling, Carlos Outeiral, Pedro Luis Cunha Farias, and Alán Aspuru-Guzik · 2017
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Graph convolutional policy network for goal-directed molecular graph generation
Jiaxuan You, Bowen Liu, Zhitao Ying, Vijay Pande, and Jure Leskovec · 2018
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Optimization of molecules via deep reinforcement learning
Zhenpeng Zhou, Steven Kearnes, Li Li, Richard N. Zare, and Patrick Riley · 2018
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