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When designing new molecules with particular properties, it is not only important what to make but crucially how to make it.
Multi-Resolution autoregressive Graph-to-Graph translation for molecules
Wengong Jin, Regina Barzilay, and Tommi S Jaakkola · 1907
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The generation of a unique machine description for chemical structures-a technique developed at chemical abstracts service
HL Morgan · 1965
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SMILES, a chemical language and information system. 1. introduction to methodology and encoding rules
David Weininger · 1988
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
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Computer-assisted planning of organic syntheses: The second generation of programs
Wolf-Dietrich Ihlenfeldt and Johann Gasteiger · 1996
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RASSE: A new method for Structure-Based drug design
Zhaowen Luo, Renxiao Wang, and Luhua Lai · 1996
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Virtual screening—an overview
W Patrick Walters, Matthew T Stahl, and Mark A Murcko · 1998
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Prediction of physicochemical parameters by atomic contributions
Scott A Wildman and Gordon M Crippen · 1999
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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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Paracetamol: a curriculum resource
Frank Ellis · 2002
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SYNOPSIS: SYNthesize and OPtimize system in silico
H Maarten Vinkers, Marc R de Jonge, Frederik FD Daeyaert, Jan Heeres, Lucien MH Koymans, Joop H van Lenthe, Paul J Lewi, Henk Timmerman, Koen Van Aken, and Paul AJ Janssen · 2003
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Virtual screening of chemical libraries
Brian K Shoichet · 2004
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A tutorial on the cross-entropy method
Pieter-Tjerk De Boer, Dirk P Kroese, Shie Mannor, and Reuven Y Rubinstein · 2005
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Structure and reaction based evaluation of synthetic accessibility
Krisztina Boda, Thomas Seidel, and Johann Gasteiger · 2007
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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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The ’wired’ universe of organic chemistry
Bartosz A Grzybowski, Kyle J M Bishop, Bartlomiej Kowalczyk, and Christopher E Wilmer · 2009
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Extended-connectivity fingerprints
David Rogers and Mathew Hahn · 2010
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Enabling future drug discovery by de novo design
Markus Hartenfeller and Gisbert Schneider · 2011
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Learning to predict chemical reactions
Matthew A Kayala, Chloé-Agathe Azencott, Jonathan H Chen, and Pierre Baldi · 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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A kernel two-sample test
Arthur Gretton, Karsten M Borgwardt, Malte J Rasch, Bernhard Schölkopf, and Alexander Smola · 2012
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DOGS: reaction-driven de novo design of bioactive compounds
Markus Hartenfeller, Heiko Zettl, Miriam Walter, Matthias Rupp, Felix Reisen, Ewgenij Proschak, Sascha Weggen, Holger Stark, and Gisbert Schneider · 2012
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ReactionPredictor: prediction of complex chemical reactions at the mechanistic level using machine learning
Matthew A Kayala and Pierre Baldi · 2012
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Extraction of chemical structures and reactions from the literature
Daniel Mark Lowe · 2012
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The enumeration of chemical space
Jean-Louis Reymond, Lars Ruddigkeit, Lorenz Blum, and Ruud van Deursen · 2012
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Auto-encoding variational Bayes
Diederik P Kingma and Max Welling · 2013
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De novo molecular design
Gisbert Schneider · 2013
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Learning phrase representations using RNN encoder–decoder for statistical machine translation
Kyunghyun Cho, Bart van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
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SCUBIDOO: a large yet screenable and easily searchable database of computationally created chemical compounds optimized toward high likelihood of synthetic tractability
Florent Chevillard and Peter Kolb · 2015
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Convolutional networks on graphs for learning molecular fingerprints
David K Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarell, Timothy Hirzel, Alán Aspuru-Guzik, and Ryan P Adams · 2015
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Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2015
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What is high-throughput virtual screening? A perspective from organic materials discovery
Edward O Pyzer-Knapp, Changwon Suh, Rafael Gómez-Bombarelli, Jorge Aguilera-Iparraguirre, and Alán Aspuru-Guzik · 2015
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Development of a novel fingerprint for chemical reactions and its application to large-scale reaction classification and similarity
Nadine Schneider, Daniel M Lowe, Roger A Sayle, and Gregory A Landrum · 2015
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Generating sentences from a continuous space
Samuel R Bowman, Luke Vilnis, Oriol Vinyals, Andrew M Dai, Rafal Jozefowicz, and Samy Bengio · 2016
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Lead Generation: Methods, Strategies, and Case Studies
Jörg Holenz · 2016
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Gated graph sequence neural networks
Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel · 2016
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Computer-assisted synthetic planning: The end of the beginning
Fréchet ChemNet Distance: A metric for generative models for molecules in drug discovery
Kristina Preuer, Philipp Renz, Thomas Unterthiner, Sepp Hochreiter, and Günter Klambauer · 2018
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“Found in Translation”: predicting outcomes of complex organic chemistry reactions using neural sequence-to-sequence models
Philippe Schwaller, Theophile Gaudin, David Lanyi, Costas Bekas, and Teodoro Laino · 2018
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Planning chemical syntheses with deep neural networks and symbolic AI
Marwin HS Segler, Mike Preuss, and Mark P Waller · 2018
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GraphVAE: Towards generation of small graphs using variational autoencoders
Martin Simonovsky and Nikos Komodakis · 2018
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Wasserstein auto-encoders
Ilya Tolstikhin, Olivier Bousquet, Sylvain Gelly, and Bernhard Schölkopf · 2018
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Virtual chemical libraries
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Sara Szymkuć, Ewa P Gajewska, Tomasz Klucznik, Karol Molga, Piotr Dittwald, Michał Startek, Michał Bajczyk, and Bartosz A Grzybowski · 2016
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Neural networks for the prediction of organic chemistry reactions
Jennifer N Wei, David Duvenaud, and Alán Aspuru-Guzik · 2016
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Tree-structured decoding with doubly-recurrent neural networks
David Alvarez-Melis and Tommi S Jaakkola · 2017
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Jug: Software for parallel reproducible computation in Python
Luis Pedro Coelho · 2017
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Prediction of organic reaction outcomes using machine learning
Connor W Coley, Regina Barzilay, Tommi S Jaakkola, William H Green, and Klavs F Jensen · 2017
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
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beta-VAE: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 2017
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W Patrick Walters · 2018
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Population-based de novo molecule generation, using grammatical evolution
Naruki Yoshikawa, Kei Terayama, Masato Sumita, Teruki Homma, Kenta Oono, and Koji Tsuda · 2018
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https://www.who.int/medicines/publications/essentialmedicines/en/
WHO | WHO model lists of essential medicines · 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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Automated de novo molecular design by hybrid machine intelligence and rule-driven chemical synthesis
Alexander Button, Daniel Merk, Jan A Hiss, and Gisbert Schneider · 2019
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A graph-convolutional neural network model for the prediction of chemical reactivity
Connor W Coley, Wengong Jin, Luke Rogers, Timothy F Jamison, Tommi S Jaakkola, William H Green, Regina Barzilay, and Klavs F Jensen · 2019
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Graph transformation policy network for chemical reaction prediction
Kien Do, Truyen Tran, and Svetha Venkatesh · 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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Molecular hypergraph grammar with its application to molecular optimization
Hiroshi Kajino · 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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RDKit: Open-source cheminformatics
RDKit, online · 2019
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NeVAE: A deep generative model for molecular graphs
Bidisha Samanta, DE Abir, Gourhari Jana, Pratim Kumar Chattaraj, Niloy Ganguly, and Manuel Gomez Rodriguez · 2019
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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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Discrete object generation with reversible inductive construction
Ari Seff, Wenda Zhou, Farhan Damani, Abigail Doyle, and Ryan P Adams · 2019
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A generative model for molecular distance geometry
Gregor NC Simm and José Miguel Hernández-Lobato · 2019
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Virtual compound libraries in computer-assisted drug discovery
Niek van Hilten, Florent Chevillard, and Peter Kolb · 2019
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D-VAE: A variational autoencoder for directed acyclic graphs
Muhan Zhang, Shali Jiang, Zhicheng Cui, Roman Garnett, and Yixin Chen · 2019
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The synthesizability of molecules proposed by generative models
Wenhao Gao and Connor W Coley · 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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Molecular design in synthetically accessible chemical space via deep reinforcement learning
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ChemBO: Bayesian optimization of small organic molecules with synthesizable recommendations
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QSAR without borders
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A deep generative model for fragment-based molecule generation
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On Failure Modes of Molecule Generators and Optimizers
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Reinforcement learning for molecular design guided by quantum mechanics
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Discovery of highly potent, selective, and orally efficacious p300/CBP histone acetyltransferases inhibitors
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