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Generating molecular graphs with desired chemical properties driven by deep graph generative models provides a very promising way to accelerate drug discovery process.
Grammar variational autoencoder. In
Matt J Kusner, Brooks Paige, and José Miguel Hernández-Lobato. 2017 · 1954
Earlier work this paper cites.
SMILES. 2. Algorithm for generation of unique SMILES notation
David Weininger, Arthur Weininger, and Joseph L Weininger. 1989 · 1989
Earlier work this paper cites.
RDKit: Open-source cheminformatics
Greg Landrum et al · 2006
Earlier work this paper cites.
How to improve R&D productivity: the pharmaceutical industry’s grand challenge
Steven M Paul, Daniel S Mytelka, Christopher T Dunwiddie, Charles C Persinger, Bernard H Munos, Stacy R Lindborg, and Aaron L Schacht. 2010 · 2010
Earlier work this paper cites.
Extended-connectivity fingerprints
David Rogers and Mathew Hahn. 2010 · 2010
Earlier work this paper cites.
Quantifying the chemical beauty of drugs
G Richard Bickerton, Gaia V Paolini, Jérémy Besnard, Sorel Muresan, and Andrew L Hopkins. 2012 · 2012
Earlier work this paper cites.
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 · 2012
Earlier work this paper cites.
Nice: Non-linear independent components estimation
Laurent Dinh, David Krueger, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
Earlier work this paper cites.
Quantum chemistry structures and properties of 134 kilo molecules
Raghunathan Ramakrishnan, Pavlo O Dral, Matthias Rupp, and O Anatole Von Lilienfeld. 2014 · 2014
Earlier work this paper cites.
The $2.6 billion pill—methodologic and policy considerations
Jerry Avorn. 2015 · 2015
Earlier work this paper cites.
Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations?
Dávid Bajusz, Anita Rácz, and Károly Héberger. 2015 · 2015
Earlier work this paper cites.
Density estimation using real nvp
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio. 2016 · 2016
Cited alongside, same era.
The drug-maker’s guide to the galaxy
Asher Mullard. 2017 · 2017
Cited alongside, same era.
Syntax-directed variational autoencoder for structured data
Hanjun Dai, Yingtao Tian, Bo Dai, Steven Skiena, and Le Song. 2018 · 2018
Cited alongside, same era.
MolGAN: An implicit generative model for small molecular graphs
Nicola De Cao and Thomas Kipf. 2018 · 2018
Cited alongside, same era.
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 · 2018
Graph convolutional policy network for goal-directed molecular graph generation. In
Jiaxuan You, Bowen Liu, Zhitao Ying, Vijay Pande, and Jure Leskovec. 2018 · 2018
Later among the works it cites.
A Two-Step Graph Convolutional Decoder for Molecule Generation
Xavier Bresson and Thomas Laurent. 2019 · 2019
Later among the works it cites.
Graph residual flow for molecular graph generation
Shion Honda, Hirotaka Akita, Katsuhiko Ishiguro, Toshiki Nakanishi, and Kenta Oono. 2019 · 2019
Later among the works it cites.
Normalizing flows: Introduction and ideas
Ivan Kobyzev, Simon Prince, and Marcus A Brubaker. 2019 · 2019
Later among the works it cites.
Graph normalizing flows. In
Jenny Liu, Aviral Kumar, Jimmy Ba, Jamie Kiros, and Kevin Swersky. 2019 · 2019
Later among the works it cites.
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Cited alongside, same era.
Junction tree variational autoencoder for molecular graph generation
Wengong Jin, Regina Barzilay, and Tommi Jaakkola. 2018 · 2018
Cited alongside, same era.
Glow: Generative flow with invertible 1x1 convolutions. In
Durk P Kingma and Prafulla Dhariwal. 2018 · 2018
Cited alongside, same era.
Constrained graph variational autoencoders for molecule design. In
Qi Liu, Miltiadis Allamanis, Marc Brockschmidt, and Alexander Gaunt. 2018 · 2018
Cited alongside, same era.
Constrained generation of semantically valid graphs via regularizing variational autoencoders. In
Tengfei Ma, Jie Chen, and Cao Xiao. 2018 · 2018
Cited alongside, same era.
Niki Parmar, Ashish Vaswani, Jakob Uszkoreit, Łukasz Kaiser, Noam Shazeer, Alexander Ku, and Dustin Tran. 2018 · 2018
Cited alongside, same era.
Modeling relational data with graph convolutional networks. In
Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, Rianne Van Den Berg, Ivan Titov, and Max Welling. 2018 · 2018
Cited alongside, same era.
Graphvae: Towards generation of small graphs using variational autoencoders. In
Martin Simonovsky and Nikos Komodakis. 2018 · 2018
Cited alongside, same era.
GraphNVP: An Invertible Flow Model for Generating Molecular Graphs
Kaushalya Madhawa, Katushiko Ishiguro, Kosuke Nakago, and Motoki Abe. 2019 · 2019
Later among the works it cites.
Normalizing Flows for Probabilistic Modeling and Inference
George Papamakarios, Eric Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, and Balaji Lakshminarayanan. 2019 · 2019
Later among the works it cites.
MolecularRNN: Generating realistic molecular graphs with optimized properties
Mariya Popova, Mykhailo Shvets, Junier Oliva, and Olexandr Isayev. 2019 · 2019
Later among the works it cites.
Graph convolutional networks for computational drug development and discovery
Mengying Sun, Sendong Zhao, Coryandar Gilvary, Olivier Elemento, Jiayu Zhou, and Fei Wang. 2019 · 2019
Later among the works it cites.
Deep learning enables rapid identification of potent DDR1 kinase inhibitors
Alex Zhavoronkov, Yan A Ivanenkov, Alex Aliper, Mark S Veselov, Vladimir A Aladinskiy, Anastasiya V Aladinskaya, Victor A Terentiev, Daniil A Polykovskiy, Maksim D Kuznetsov, et al · 2019
Later among the works it cites.
GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation
Chence Shi, Minkai Xu, Zhaocheng Zhu, Weinan Zhang, Ming Zhang, and Jian. Tang. 2020 · 2020
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