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The problem of molecular generation has received significant attention recently.
On certain formal properties of grammars
Noam Chomsky · 1959
Earlier work this paper cites.
Structure-mapping: A theoretical framework for analogy
Dedre Gentner · 1983
Earlier work this paper cites.
Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules
David Weininger · 1988
Earlier work this paper cites.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
Earlier work this paper cites.
Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions
Peter Ertl and Ansgar Schuffenhauer · 2009
Earlier work this paper cites.
Extended-connectivity fingerprints
David Rogers and Mathew Hahn · 2010
Earlier work this paper cites.
Bayesian reasoning and machine learning
David Barber · 2012
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
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
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
Earlier work this paper cites.
Computational modeling of β \beta -secretase 1 (bace-1) inhibitors using ligand based approaches
Govindan Subramanian, Bharath Ramsundar, Vijay Pande, and Rajiah Aldrin Denny · 2016
Earlier work this paper cites.
Low data drug discovery with one-shot learning
Han Altae-Tran, Bharath Ramsundar, Aneesh S Pappu, and Vijay Pande · 2017
Earlier work this paper cites.
Task-based end-to-end model learning in stochastic optimization
Priya L Donti, Brandon Amos, and J Zico Kolter · 2017
Earlier work this paper cites.
A point set generation network for 3d object reconstruction from a single image
Haoqiang Fan, Hao Su, and Leonidas J Guibas · 2017
Earlier work this paper cites.
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
Earlier work this paper cites.
Molecular de-novo design through deep reinforcement learning
Marcus Olivecrona, Thomas Blaschke, Ola Engkvist, and Hongming Chen · 2017
Earlier work this paper cites.
Optimizing distributions over molecular space. an objective-reinforced generative adversarial network for inverse-design chemistry (organic)
Benjamin Sanchez-Lengeling, Carlos Outeiral, Gabriel L Guimaraes, and Alan Aspuru-Guzik · 2017
Earlier work this paper cites.
Chemts: an efficient python library for de novo molecular generation
Xiufeng Yang, Jinzhe Zhang, Kazuki Yoshizoe, Kei Terayama, and Koji Tsuda · 2017
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Learning hyperedge replacement grammars for graph generation
Salvador Aguinaga, David Chiang, and Tim Weninger · 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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Molgan: An implicit generative model for small molecular graphs
Nicola De Cao and Thomas Kipf · 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
Efficient graph generation with graph recurrent attention networks
Renjie Liao, Yujia Li, Yang Song, Shenlong Wang, Charlie Nash, William L Hamilton, David Duvenaud, Raquel Urtasun, and Richard S Zemel · 2019
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Graphnvp: An invertible flow model for generating molecular graphs
Kaushalya Madhawa, Katushiko Ishiguro, Kosuke Nakago, and Motoki Abe · 2019
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Hierarchical machine learning model for mechanical property predictions of polyurethane elastomers from small datasets
Aditya Menon, James A Thompson-Colón, and Newell R Washburn · 2019
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De novo molecular design by combining deep autoencoder recurrent neural networks with generative topographic mapping
Boris Sattarov, Igor I Baskin, Dragos Horvath, Gilles Marcou, Esben Jannik Bjerrum, and Alexandre Varnek · 2019
Later among the works it cites.
Modeling graphs with vertex replacement grammars
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Junction tree variational autoencoder for molecular graph generation
Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2018
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Learning deep generative models of graphs
Yujia Li, Oriol Vinyals, Chris Dyer, Razvan Pascanu, and Peter Battaglia · 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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Constrained generation of semantically valid graphs via regularizing variational autoencoders
Tengfei Ma, Jie Chen, and Cao Xiao · 2018
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Two decades under the influence of the rule of five and the changing properties of approved oral drugs: miniperspective
Michael D Shultz · 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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Satyaki Sikdar, Justus Hibshman, and Tim Weninger · 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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Deep learning for molecular generation
Youjun Xu, Kangjie Lin, Shiwei Wang, Lei Wang, Chenjing Cai, Chen Song, Luhua Lai, and Jianfeng Pei · 2019
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Retro*: learning retrosynthetic planning with neural guided a* search
Binghong Chen, Chengtao Li, Hanjun Dai, and Le Song · 2020
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Hierarchical generation of molecular graphs using structural motifs
Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2020
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Pi1m: a benchmark database for polymer informatics
Ruimin Ma and Tengfei Luo · 2020
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Mol-cyclegan: a generative model for molecular optimization
Łukasz Maziarka, Agnieszka Pocha, Jan Kaczmarczyk, Krzysztof Rataj, Tomasz Danel, and Michał Warchoł · 2020
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Molecular sets (moses): a benchmarking platform for molecular generation models
Daniil Polykovskiy, Alexander Zhebrak, Benjamin Sanchez-Lengeling, Sergey Golovanov, Oktai Tatanov, Stanislav Belyaev, Rauf Kurbanov, Aleksey Artamonov, Vladimir Aladinskiy, Mark Veselov, et al · 2020
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Nevae: A deep generative model for molecular graphs
Bidisha Samanta, Abir De, Gourhari Jana, Vicenç Gómez, Pratim Kumar Chattaraj, Niloy Ganguly, and Manuel Gomez-Rodriguez · 2020
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Polygrammar: Grammar for digital polymer representation and generation
Minghao Guo, Wan Shou, Liane Makatura, Timothy Erps, Michael Foshey, and Wojciech Matusik · 2021
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Beyond generative models: superfast traversal, optimization, novelty, exploration and discovery (stoned) algorithm for molecules using selfies
AkshatKumar Nigam, Robert Pollice, Mario Krenn, Gabriel dos Passos Gomes, and Alan Aspuru-Guzik · 2021
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Fs-mol: A few-shot learning dataset of molecules
Megan Stanley, John F Bronskill, Krzysztof Maziarz, Hubert Misztela, Jessica Lanini, Marwin Segler, Nadine Schneider, and Marc Brockschmidt · 2021
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