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De novo molecular design attempts to search over the chemical space for molecules with the desired property.
Elementary mathematical theory of classification and prediction
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Method and apparatus for designing molecules with desired properties by evolving successive populations, July 18 1995
David Weininger · 1995
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The art and practice of structure-based drug design: a molecular modeling perspective
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Chemical similarity searching
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Automatic molecular design using evolutionary techniques
Al Globus, John Lawton, and Todd Wipke · 1999
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A genetic algorithm for the automated generation of small organic molecules: Drug design using an evolutionary algorithm
Dominique Douguet, Etienne Thoreau, and Gérard Grassy · 2000
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De novo design of molecular architectures by evolutionary assembly of drug-derived building blocks
Gisbert Schneider, Man-Ling Lee, Martin Stahl, and Petra Schneider · 2000
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Handbook of essential pharmacokinetics, pharmacodynamics and drug metabolism for industrial scientists
Younggil Kwon · 2001
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Virtual screening of chemical libraries
Brian K Shoichet · 2004
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A graph-based genetic algorithm and its application to the multiobjective evolution of median molecules
Nathan Brown, Ben McKay, François Gilardoni, and Johann Gasteiger · 2004
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A simple and effective evolutionary algorithm for the vehicle routing problem
Christian Prins · 2004
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Computer-based de novo design of drug-like molecules
Gisbert Schneider and Uli Fechner · 2005
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Chemoinformatics: a textbook
Johann Gasteiger and Thomas Engel · 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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Second-generation de novo design: a view from a medicinal chemist perspective
Andrea Zaliani, Krisztina Boda, Thomas Seidel, Achim Herwig, Christof H Schwab, Johann Gasteiger, Holger Claußen, Christian Lemmen, Jörg Degen, Juri Pärn, et al · 2009
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Principles of early drug discovery
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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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Chembl: a large-scale bioactivity database for drug discovery
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Frnakenstein: multiple target inverse rna folding
Rune B Lyngsø, James WJ Anderson, Elena Sizikova, Amarendra Badugu, Tomas Hyland, and Jotun Hein · 2012
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De novo molecular design
Gisbert Schneider · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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General program synthesis benchmark suite
Thomas Helmuth and Lee Spector · 2015
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Neural combinatorial optimization with reinforcement learning
Irwan Bello, Hieu Pham, Quoc V Le, Mohammad Norouzi, and Samy Bengio · 2016
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Automatic chemical design using a data-driven continuous representation of molecules, 2016
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 · 2016
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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, et al · 2016
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Objective-reinforced generative adversarial networks (organ) for sequence generation models
Applying machine learning techniques to predict the properties of energetic materials
Daniel C Elton, Zois Boukouvalas, Mark S Butrico, Mark D Fuge, and Peter W Chung · 2018
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Scifinder
Stephen Walter Gabrielson · 2018
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Drugbank 5.0: a major update to the drugbank database for 2018
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A model to search for synthesizable molecules
John Bradshaw, Brooks Paige, Matt J Kusner, Marwin Segler, and José Miguel Hernández-Lobato · 2019
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Molecularrnn: Generating realistic molecular graphs with optimized properties
Mariya Popova, Mykhailo Shvets, Junier Oliva, and Olexandr Isayev · 2019
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Gabriel Lima Guimaraes, Benjamin Sanchez-Lengeling, Carlos Outeiral, Pedro Luis Cunha Farias, and Alán Aspuru-Guzik · 2017
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Grammar variational autoencoder
Matt J Kusner, Brooks Paige, and José Miguel Hernández-Lobato · 2017
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Neural symbolic machines: Learning semantic parsers on freebase with weak supervision
Chen Liang, Jonathan Berant, Quoc Le, Kenneth Forbus, and Ni Lao · 2017
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Thinking fast and slow with deep learning and tree search
Thomas Anthony, Zheng Tian, and David Barber · 2017
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Mastering the game of go without human knowledge
David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, et al · 2017
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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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Non-fullerene acceptors for organic solar cells
Cenqi Yan, Stephen Barlow, Zhaohui Wang, He Yan, Alex K-Y Jen, Seth R Marder, and Xiaowei Zhan · 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
Cited alongside, same era.
Robin Winter, Floriane Montanari, Andreas Steffen, Hans Briem, Frank Noé, and Djork-Arné Clevert · 2019
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Optimization of molecules via deep reinforcement learning
Zhenpeng Zhou, Steven Kearnes, Li Li, Richard N Zare, and Patrick Riley · 2019
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A graph-based genetic algorithm and generative model/monte carlo tree search for the exploration of chemical space
Jan H Jensen · 2019
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Feedback gan for dna optimizes protein functions
Anvita Gupta and James Zou · 2019
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Learning to generalize from sparse and underspecified rewards
Rishabh Agarwal, Chen Liang, Dale Schuurmans, and Mohammad Norouzi · 2019
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Guacamol: benchmarking models for de novo molecular design
Nathan Brown, Marco Fiscato, Marwin HS Segler, and Alain C Vaucher · 2019
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Conditioning by adaptive sampling for robust design
David Brookes, Hahnbeom Park, and Jennifer Listgarten · 2019
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Policy gradient search: Online planning and expert iteration without search trees
Thomas Anthony, Robert Nishihara, Philipp Moritz, Tim Salimans, and John Schulman · 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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Learning multimodal graph-to-graph translation for molecule optimization
Wengong Jin, Kevin Yang, Regina Barzilay, and Tommi Jaakkola · 2019
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The ionic liquid property explorer: An extensive library of task-specific solvents
Vishwesh Venkatraman, Sigvart Evjen, and Kallidanthiyil Chellappan Lethesh · 2019
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Graphaf: a flow-based autoregressive model for molecular graph generation
Chence Shi, Minkai Xu, Zhaocheng Zhu, Weinan Zhang, Ming Zhang, and Jian Tang · 2020
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Constrained bayesian optimization for automatic chemical design using variational autoencoders
Ryan-Rhys Griffiths and José Miguel Hernández-Lobato · 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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Learning to navigate the synthetically accessible chemical space using reinforcement learning
Sai Krishna Gottipati, Boris Sattarov, Sufeng Niu, Yashaswi Pathak, Haoran Wei, Shengchao Liu, Karam MJ Thomas, Simon Blackburn, Connor W Coley, Jian Tang, et al · 2020
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Augmenting genetic algorithms with deep neural networks for exploring the chemical space
AkshatKumar Nigam, Pascal Friederich, Mario Krenn, and Alan Aspuru-Guzik · 2020
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Crem: chemically reasonable mutations framework for structure generation
Pavel Polishchuk · 2020
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The synthesizability of molecules proposed by generative models
Wenhao Gao and Connor W Coley · 2020
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Model-based reinforcement learning for biological sequence design
Christof Angermueller, David Dohan, David Belanger, Ramya Deshpande, Kevin Murphy, and Lucy Colwell · 2020
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