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Variational autoencoder (VAE) is a popular method for drug discovery and there had been a great deal of architectures and pipelines proposed to improve its performance.
Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules
David Weininger · 1988
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Chemical graph theory: introduction and fundamentals , volume 1
Danail Bonchev · 1991
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The properties of known drugs. 1. molecular frameworks
Guy W Bemis and Mark A Murcko · 1996
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Prediction of physicochemical parameters by atomic contributions
Scott A Wildman and Gordon M Crippen · 1999
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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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On the art of compiling and using’drug-like’chemical fragment spaces
Jörg Degen, Christof Wegscheid-Gerlach, Andrea Zaliani, and Matthias Rarey · 2008
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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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Extended-connectivity fingerprints
David Rogers and Mathew Hahn · 2010
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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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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Convolutional networks on graphs for learning molecular fingerprints
David Duvenaud, Dougal Maclaurin, Jorge Aguilera-Iparraguirre, Rafael Gómez-Bombarelli, Timothy Hirzel, Alán Aspuru-Guzik, and Ryan P Adams · 2015
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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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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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Grammar variational autoencoder
Matt J Kusner, Brooks Paige, and José Miguel Hernández-Lobato · 2017
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Application of generative autoencoder in de novo molecular design
Thomas Blaschke, Marcus Olivecrona, Ola Engkvist, Jürgen Bajorath, and Hongming Chen · 2018
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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
Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules
Niklas Gebauer, Michael Gastegger, and Kristof Schütt · 2019
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Hierarchical graph-to-graph translation for molecules
Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2019
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Chembl: towards direct deposition of bioassay data
David Mendez, Anna Gaulton, A Patrícia Bento, Jon Chambers, Marleen De Veij, Eloy Félix, María Paula Magariños, Juan F Mosquera, Prudence Mutowo, Michał Nowotka, et al · 2019
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A de novo molecular generation method using latent vector based generative adversarial network
Oleksii Prykhodko, Simon Viet Johansson, Panagiotis-Christos Kotsias, Josep Arús-Pous, Esben Jannik Bjerrum, Ola Engkvist, and Hongming Chen · 2019
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Message-passing neural networks for high-throughput polymer screening
Peter C St. John, Caleb Phillips, Travis W Kemper, A Nolan Wilson, Yanfei Guan, Michael F Crowley, Mark R Nimlos, and Ross E Larsen · 2019
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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 L Gaunt · 2018
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Fréchet chemnet distance: a metric for generative models for molecules in drug discovery
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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 · 2018
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Graphvae: Towards generation of small graphs using variational autoencoders
Martin Simonovsky and Nikos Komodakis · 2018
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Graph convolutional policy network for goal-directed molecular graph generation
Jiaxuan You, Bowen Liu, Rex Ying, Vijay Pande, and Jure Leskovec · 2018
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Diagnosing and enhancing vae models
Bin Dai and David Wipf · 2019
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Molecular sets (moses): a benchmarking platform for molecular generation models
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Variational autoencoders in the presence of low-dimensional data: landscape and implicit bias
Frederic Koehler, Viraj Mehta, Andrej Risteski, and Chenghui Zhou · 2021
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A 3d generative model for structure-based drug design
Shitong Luo, Jiaqi Guan, Jianzhu Ma, and Jian Peng · 2021
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E (n) equivariant normalizing flows
Victor Garcia Satorras, Emiel Hoogeboom, Fabian B Fuchs, Ingmar Posner, and Max Welling · 2021
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Inverse design of 3d molecular structures with conditional generative neural networks
Niklas WA Gebauer, Michael Gastegger, Stefaan SP Hessmann, Klaus-Robert Müller, and Kristof T Schütt · 2022
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Equivariant diffusion for molecule generation in 3d
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Geodiff: A geometric diffusion model for molecular conformation generation
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