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Automatic design with machine learning and molecular simulations has shown a remarkable ability to generate new and promising drug candidates.
SMILES, a chemical language and information system. 1. introduction to methodology and encoding rules
David Weininger · 1988
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Recap - retrosynthetic combinatorial analysis procedure: A powerful new technique for identifying privileged molecular fragments with useful applications in combinatorial chemistry
Xiao Qing Lewell, Duncan B Judd, Stephen P Watson, and Michael M Hann · 1998
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A global geometric framework for nonlinear dimensionality reduction
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Foundations in grammatical evolution for dynamic environments
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Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions
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Extended-connectivity fingerprints
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Directory of useful decoys, enhanced (DUD-E): Better ligands and decoys for better benchmarking
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ZINC: A free tool to discover chemistry for biology
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Auto-encoding variational bayes
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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
Generating focused molecule libraries for drug discovery with recurrent neural networks
Marwin H. S. Segler, Thierry Kogej, Christian Tyrchan, and Mark P. Waller · 2017
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Deep reinforcement learning for de-novo drug design
Mariya Popova, Olexandr Isayev, and Alexander Tropsha · 2017
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Bayesian molecular design with a chemical language model
Hisaki Ikebata, Kenta Hongo, Tetsu Isomura, Ryo Maezono, and Ryo Yoshida · 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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Chemgan challenge for drug discovery: can ai reproduce natural chemical diversity?
Mostapha Benhenda · 2017
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Grammar variational autoencoder
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Objective-reinforced generative adversarial networks (ORGAN) for sequence generation models
Gabriel Lima Guimaraes, Benjamin Sanchez-Lengeling, Pedro Luis Cunha Farias, and Alán Aspuru-Guzik · 2017
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OpenSMILES specification
Craig A. James
Cited in the paper.
http://www.rdkit.org
RDKit: Open-source cheminformatics
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Population based training of neural networks
Max Jaderberg, Valentin Dalibard, Simon Osindero, Wojciech M Czarnecki, Jeff Donahue, Ali Razavi, Oriol Vinyals, Tim Green, Iain Dunning, Karen Simonyan, et al · 2017
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Information-geometric optimization algorithms: A unifying picture via invariance principles
Yann Ollivier, Ludovic Arnold, Anne Auger, and Nikolaus Hansen · 2017
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