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Designing new molecules with a set of predefined properties is a core problem in modern drug discovery and development.
A class of gradient-estimation algorithms for reinforcement learning in neural networks
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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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Computer-based de novo design of drug-like molecules
Gisbert Schneider and Uli Fechner · 2005
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Zinc - a free database of commercially available compounds for virtual screening
John J Irwin and Brian K Shoichet · 2005
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Rdkit: Open-source cheminformatics, 2006
Greg Landrum et al · 2006
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Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions
Peter Ertl and Ansgar Schuffenhauer · 2009
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Chembl: a large-scale bioactivity database for drug discovery
A. Gaulton, L.J. Bellis, A.P. Bento, J. Chambers, M. Davies, A. Hersey, Y. Light, S. McGlinchey, D. Michalovich, B. Al-Lazikani, and J.P. Overington · 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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How accurately can we predict the melting points of drug-like compounds?
Y. Tetko, I.V. ans Sushko, S. Novotarskyi, L. Patiny, I. Kondratov, A.E. Petrenko, L. Charochkina, and A.M. Asiri · 2014
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
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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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Generating focused molecule libraries for drug discovery with recurrent neural networks
Marwin HS Segler, Thierry Kogej, Christian Tyrchan, and Mark P Waller · 2017
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Machine learning for molecular and materials science
Keith T Butler, Daniel W Davies, Hugh Cartwright, Olexandr Isayev, and Aron Walsh · 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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Deep reinforcement learning for de novo drug design
Mariya Popova, Olexandr Isayev, and Alexander Tropsha · 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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Constrained graph variational autoencoders for molecule design
Qi Liu, Miltiadis Allamanis, Marc Brockschmidt, and Alexander Gaunt · 2018
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drugan: an advanced generative adversarial autoencoder model for de novo generation of new molecules with desired molecular properties in silico
Artur Kadurin, Sergey Nikolenko, Kuzma Khrabrov, Alex Aliper, and Alex Zhavoronkov · 2017
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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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Learning deep generative models of graphs
Yujia Li, Oriol Vinyals, Chris Dyer, Razvan Pascanu, and Peter Battaglia
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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
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Multi-objective de novo drug design with conditional graph generative model
Yibo Li, Liangren Zhang, and Zhenming Liu
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Graphrnn: Generating realistic graphs with deep auto-regressive models
Jiaxuan You, Rex Ying, Xiang Ren, William L Hamilton, and Jure Leskovec
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Daniil Polykovskiy, Alexander Zhebrak, Benjamin Sanchez-Lengeling, Sergey Golovanov, Oktai Tatanov, Stanislav Belyaev, Rauf Kurbanov, Aleksey Artamonov, Vladimir Aladinskiy, Mark Veselov, et al · 2018
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