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Traditional molecular string representations, such as SMILES, often pose challenges for AI-driven molecular design due to their non-sequential depiction of molecular substructures.
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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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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Computationally efficient algorithm to identify matched molecular pairs (mmps) in large data sets
Jameed Hussain and Ceara Rea · 2010
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Unichem: a unified chemical structure cross-referencing and identifier tracking system
Jon Chambers, Mark Davies, Anna Gaulton, Anne Hersey, Sameer Velankar, Robert Petryszak, Janna Hastings, Louisa Bellis, Shaun McGlinchey, and John P Overington · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Central nervous system multiparameter optimization desirability: application in drug discovery
Travis T Wager, Xinjun Hou, Patrick R Verhoest, and Anabella Villalobos · 2016
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Deepsmiles: An adaptation of smiles for use in machine-learning of chemical structures
Dalke A. O’Boyle N · 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
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Junction tree variational autoencoder for molecular graph generation
Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2018
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Epidermal growth factor receptor tyrosine kinase inhibitors for central nervous system metastases from non-small cell lung cancer
Manmeet S Ahluwalia, Kevin Becker, and Benjamin P Levy · 2018
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Fast lexically constrained decoding with dynamic beam allocation for neural machine translation
Matt Post and David Vilar · 2018
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Molecular transformer: a model for uncertainty-calibrated chemical reaction prediction
Philippe Schwaller, Teodoro Laino, Théophile Gaudin, Peter Bolgar, Christopher A Hunter, Costas Bekas, and Alpha A Lee · 2019
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Self-referencing embedded strings (selfies): A 100% robust molecular string representation
Mario Krenn, Florian Häse, AkshatKumar Nigam, Pascal Friederich, and Alan Aspuru-Guzik · 2020
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Scaffold-constrained molecular generation
Maxime Langevin, Hervé Minoux, Maximilien Levesque, and Marc Bianciotto · 2020
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Multi-constraint molecular generation based on conditional transformer, knowledge distillation and reinforcement learning
Jike Wang, Chang-Yu Hsieh, Mingyang Wang, Xiaorui Wang, Zhenxing Wu, Dejun Jiang, Benben Liao, Xujun Zhang, Bo Yang, Qiaojun He, et al · 2021
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Therapeutics data commons: Machine learning datasets and tasks for drug discovery and development
Kexin Huang, Tianfan Fu, Wenhao Gao, Yue Zhao, Yusuf Roohani, Jure Leskovec, Connor W Coley, Cao Xiao, Jimeng Sun, and Marinka Zitnik · 2021
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Selfies and the future of molecular string representations
Mario Krenn, Qianxiang Ai, Senja Barthel, Nessa Carson, Angelo Frei, Nathan C Frey, Pascal Friederich, Théophile Gaudin, Alberto Alexander Gayle, Kevin Maik Jablonka, et al · 2022
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Generative models for molecular discovery: Recent advances and challenges
Camille Bilodeau, Wengong Jin, Tommi Jaakkola, Regina Barzilay, and Klavs F. Jensen · 2022
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Molgensurvey: A systematic survey in machine learning models for molecule design
Yuanqi Du, Tianfan Fu, Jimeng Sun, and Shengchao Liu · 2022
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Smiles-based deep generative scaffold decorator for de-novo drug design
Josep Arús-Pous, Atanas Patronov, Esben Jannik Bjerrum, Christian Tyrchan, Jean-Louis Reymond, Hongming Chen, and Ola Engkvist · 2020
Cited alongside, same era.
Molecular representations in ai-driven drug discovery: a review and practical guide
Laurianne David, Amol Thakkar, Rocío Mercado, and Ola Engkvist · 2020
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Multi-objective molecule generation using interpretable substructures
Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 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, Artur Kadurin, Simon Johansson, Hongming Chen, Sergey Nikolenko, Alán Aspuru-Guzik, and Alex Zhavoronkov · 2020
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Libinvent: reaction-based generative scaffold decoration for in silico library design
Vendy Fialková, Jiaxi Zhao, Kostas Papadopoulos, Ola Engkvist, Esben Jannik Bjerrum, Thierry Kogej, and Atanas Patronov · 2021
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Transformer-based generative model accelerating the development of novel braf inhibitors
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Viraj Bagal, Rishal Aggarwal, PK Vinod, and U Deva Priyakumar · 2021
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Transformer-based molecular optimization beyond matched molecular pairs
Jiazhen He, Eva Nittinger, Christian Tyrchan, Werngard Czechtizky, Atanas Patronov, Esben Jannik Bjerrum, and Ola Engkvist · 2022
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Gensmiles: An enhanced validity conscious representation for inverse design of molecules
Arun Singh Bhadwal, Kamal Kumar, and Neeraj Kumar · 2023
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Group selfies: a robust fragment-based molecular string representation
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