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Designing compounds with desired properties is a key element of the drug discovery process.
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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ImageNet: A Large-Scale Hierarchical Image Database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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Extended-connectivity fingerprints
David Rogers and Mathew Hahn · 2010
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Dsx: A knowledge-based scoring function for the assessment of protein–ligand complexes
Gerd Neudert and Gerhard Klebe · 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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Auto-encoding variational bayes, 2013
Diederik P Kingma and Max Welling · 2013
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Lessons learned in empirical scoring with smina from the csar 2011 benchmarking exercise
David Ryan Koes, Matthew P. Baumgartner, and Carlos J. Camacho · 2013
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rdock: A fast, versatile and open source program for docking ligands to proteins and nucleic acids
Sergio Ruiz-Carmona, Daniel Alvarez-Garcia, Nicolas Foloppe, A. Beatriz Garmendia-Doval, Szilveszter Juhos, Peter Schmidtke, Xavier Barril, Roderick E. Hubbard, and S. David Morley · 2014
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Computational methods in drug discovery
Gregory Sliwoski, Sandeepkumar Kothiwale, Jens Meiler, and Edward W. Lowe · 2014
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Learning from the harvard clean energy project: The use of neural networks to accelerate materials discovery
Edward O. Pyzer-Knapp, Kewei Li, and Alan Aspuru-Guzik · 2015
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The ChEMBL database in 2017
Anna Gaulton, Anne Hersey, Michał Nowotka, A. Patrícia Bento, Jon Chambers, David Mendez, Prudence Mutowo, Francis Atkinson, Louisa J. Bellis, Elena Cibrián-Uhalte, Mark Davies, Nathan Dedman, Anneli Karlsson, María Paula Magariños, John P. Overington, George Papadatos, Ines Smit, and Andrew R. Leach · 2016
Cited alongside, same era.
Automatic chemical design using a data-driven continuous representation of molecules
Rafael Gómez-Bombarelli, David Duvenaud, José Miguel Hernández-Lobato, Jorge Aguilera-Iparraguirre, Timothy D. Hirzel, Ryan P. Adams, and Alán Aspuru-Guzik · 2016
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Rdkit: Open-source cheminformatics software
Greg Landrum · 2016
Cited alongside, same era.
Grammar variational autoencoder, 2017
Matt J. Kusner, Brooks Paige, and José Miguel Hernández-Lobato · 2017
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Generating focused molecule libraries for drug discovery with recurrent neural networks
Marwin H. S. Segler, Thierry Kogej, Christian Tyrchan, and Mark P. Waller · 2018
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Hunting for Organic Molecules with Artificial Intelligence: Molecules Optimized for Desired Excitation Energies
Masato Sumita, Xiufeng Yang, Shinsuke Ishihara, Ryo Tamura, and Koji Tsuda · 2018
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GLUE: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman · 2018
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Graph convolutional policy network for goal-directed molecular graph generation
Jiaxuan You, Bowen Liu, Rex Ying, Vijay S. Pande, and Jure Leskovec · 2018
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Guacamol: Benchmarking models for de novo molecular design
Nathan Brown, Marco Fiscato, Marwin H.S. Segler, and Alain C. Vaucher · 2019
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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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Latent molecular optimization for targeted therapeutic design
Tristan Aumentado-Armstrong · 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
Cited alongside, same era.
Junction tree variational autoencoder for molecular graph generation
Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2018
Cited alongside, same era.
Autonomous discovery in the chemical sciences part ii: Outlook
Connor W Coley, Natalie S Eyke, and Klavs F. Jensen · 2019
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Deep learning for molecular design—a review of the state of the art
Daniel C. Elton, Zois Boukouvalas, Mark D. Fuge, and Peter W. Chung · 2019
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Automated de novo drug design: Are we nearly there yet?
Gisbert Schneider and David E. Clark · 2019
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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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