Generative models are becoming a tool of choice for exploring the molecular space.
Reading the bibliography…
The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology
Artur Kadurin, Alexander Aliper, Andrey Kazennov, Polina Mamoshina, Quentin Vanhaelen, Kuzma Khrabrov, and Alex Zhavoronkov · 1949
Earlier work this paper cites.
Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules
David Weininger · 1988
Earlier work this paper cites.
Smiles. 2. algorithm for generation of unique smiles notation
David Weininger, Arthur Weininger, and Joseph L Weininger · 1989
Earlier work this paper cites.
The properties of known drugs. 1. molecular frameworks
G W Bemis and M A Murcko · 1996
Earlier work this paper cites.
A new atom-additive method for calculating partition coefficients
Renxiao Wang, Ying Fu, and Luhua Lai · 1997
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
Bidirectional recurrent neural networks
M Schuster and K K Paliwal · 1997
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
Prediction of physicochemical parameters by atomic contributions
Scott A Wildman and Gordon M Crippen · 1999
Earlier work this paper cites.
The design of leadlike combinatorial libraries
Simon J Teague, Andrew M Davis, Paul D Leeson, and Tudor Oprea · 1999
Earlier work this paper cites.
An open standard for chemical structure representation: The iupac chemical identifier
Stephen E Stein, Stephen R Heller, and Dmitrii V Tchekhovskoi · 2003
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Chemical space
Peter Kirkpatrick and Clare Ellis · 2004
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A comprehensive listing of bioactivation pathways of organic functional groups
Amit S Kalgutkar, Iain Gardner, R Scott Obach, Christopher L Shaffer, Ernesto Callegari, Kirk R Henne, Abdul E Mutlib, Deepak K Dalvie, Jae S Lee, Yasuhiro Nakai, John P O’Donnell, Jason Boer, and Shawn P Harriman · 2005
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Minimising the potential for metabolic activation in drug discovery
Amit S Kalgutkar and John R Soglia · 2005
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RDKit: Open-source cheminformatics
Greg Landrum · 2006
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On the art of compiling and using ’drug-like’ chemical fragment spaces
J Degen, C Wegscheid-Gerlach, A Zaliani, and M Rarey · 2008
Earlier work this paper cites.
Emergent strategies for inverse molecular design
Xiangqian Hu, David N Beratan, and Weitao Yang · 2009
Earlier work this paper cites.
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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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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Extended-connectivity fingerprints
David Rogers and Mathew Hahn · 2010
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New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays
Jonathan B Baell and Georgina A Holloway · 2010
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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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Scalable k-means+
Bahman Bahmani, Benjamin Moseley, Andrea Vattani, Ravi Kumar, and Sergei Vassilvitskii · 2012
Earlier work this paper cites.
The high-throughput highway to computational materials design
Stefano Curtarolo, Gus L W Hart, Marco Buongiorno Nardelli, Natalio Mingo, Stefano Sanvito, and Ohad Levy · 2013
Earlier work this paper cites.
Auto-Encoding Variational Bayes
Diederik P Kingma and Max Welling · 2013
Earlier work this paper cites.
Quantum chemistry structures and properties of 134 kilo molecules
Raghunathan Ramakrishnan, Pavlo O Dral, Matthias Rupp, and O Anatole von Lilienfeld · 2014
Earlier work this paper cites.
Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
Earlier work this paper cites.
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
Earlier work this paper cites.
The chemical space project
Jean-Louis Reymond · 2015
Cited alongside, same era.
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
Cited alongside, same era.
Convolutional networks on graphs for learning molecular fingerprints
David K Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarell, Timothy Hirzel, Alan Aspuru-Guzik, and Ryan P Adams · 2015
Cited alongside, same era.
ZINC 15 - ligand discovery for everyone
Teague Sterling and John J Irwin · 2015
Cited alongside, same era.
Adam: A method of stochastic optimization
Diederik P Kingma and Jimmy Lei Ba · 2015
Cited alongside, same era.
Discovery and optimization of materials using evolutionary approaches
Tu C Le and David A Winkler · 2016
Cited alongside, same era.
Applications of deep learning in biomedicine
Polina Mamoshina, Armando Vieira, Evgeny Putin, and Alex Zhavoronkov · 2016
Cited alongside, same era.
Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data
A. Aliper, S. Plis, A. Artemov, A. Ulloa, P. Mamoshina, and A. Zhavoronkov · 2016
Cited alongside, same era.
Sequence tutor: Conservative Fine-Tuning of sequence generation models with KL-control
Natasha Jaques, Shixiang Gu, Dzmitry Bahdanau, José Miguel Hernández-Lobato, Richard E Turner, and Douglas Eck · 2016
Cited alongside, same era.
Adversarial autoencoders
Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly, and Ian Goodfellow · 2016
Cited alongside, same era.
Filtering promiscuous compounds in early drug discovery: is it a good idea?
Mario R Senger, Carlos AM Fraga, Rafael F Dantas, and Floriano P Silva Jr · 2016
Cited alongside, same era.
Seqgan: Sequence generative adversarial nets with policy gradient
Lantao Yu, Weinan Zhang, Jun Wang, and Yong Yu · 2017
Cited alongside, same era.
Entangled conditional adversarial autoencoder for de novo drug discovery
Daniil Polykovskiy, Alexander Zhebrak, Dmitry Vetrov, Yan Ivanenkov, Vladimir Aladinskiy, Polina Mamoshina, Marine Bozdaganyan, Alexander Aliper, Alex Zhavoronkov, and Artur Kadurin · 2018
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Designing anticancer peptides by constructive machine learning
Francesca Grisoni, Claudia S Neuhaus, Gisela Gabernet, Alex T Müller, Jan A Hiss, and Gisbert Schneider · 2018
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Progressive growing of gans for improved quality, stability, and variation
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen · 2018
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Artificial intelligence for aging and longevity research: Recent advances and perspectives
Alex Zhavoronkov, Polina Mamoshina, Quentin Vanhaelen, Morten Scheibye-Knudsen, Alexey Moskalev, and Alex Aliper · 2018
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Inverse molecular design using machine learning: Generative models for matter engineering
Benjamin Sanchez-Lengeling and Alán Aspuru-Guzik · 2018
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Machine learning on human muscle transcriptomic data for biomarker discovery and tissue-specific drug target identification
Polina Mamoshina, Marina Volosnikova, Ivan V. Ozerov, Evgeny Putin, Ekaterina Skibina, Franco Cortese, and Alex Zhavoronkov · 2018
Closest in time.
MoleculeNet: a benchmark for molecular machine learning
Zhenqin Wu, Bharath Ramsundar, Evan N Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S Pappu, Karl Leswing, and Vijay Pande · 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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MolGAN: An implicit generative model for small molecular graphs
Nicola De Cao and Thomas Kipf · 2018
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Deep reinforcement learning for de novo drug design
Mariya Popova, Olexandr Isayev, and Alexander Tropsha · 2018
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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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Conditional molecular design with deep generative models
Seokho Kang and Kyunghyun Cho · 2018
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Adversarial threshold neural computer for molecular de novo design
Evgeny Putin, Arip Asadulaev, Quentin Vanhaelen, Yan Ivanenkov, Anastasia V Aladinskaya, Alex Aliper, and Alex Zhavoronkov · 2018
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DeepSMILES: An Adaptation of SMILES for Use in Machine-Learning of Chemical Structures
Noel O’Boyle and Andrew Dalke · 2018
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Syntax-directed variational autoencoder for structured data
Hanjun Dai, Yingtao Tian, Bo Dai, Steven Skiena, and Le Song · 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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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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Two decades under the influence of the rule of five and the changing properties of approved oral drugs: Miniperspective
Michael D Shultz · 2018
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Application of generative autoencoder in de novo molecular design
Thomas Blaschke, Marcus Olivecrona, Ola Engkvist, Jürgen Bajorath, and Hongming Chen · 2018
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Improving chemical autoencoder latent space and molecular de novo generation diversity with heteroencoders
Esben Bjerrum and Boris Sattarov · 2018
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Identification of novel antibacterials using machine learning techniques
Yan A Ivanenkov, Alex Zhavoronkov, Renat S Yamidanov, Ilya A Osterman, Petr V Sergiev, Vladimir A Aladinskiy, Anastasia V Aladinskaya, Victor A Terentiev, Mark S Veselov, Andrey A Ayginin, et al · 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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Virtual compound libraries in computer-assisted drug discovery
Niek van Hilten, Florent Chevillard, and Peter Kolb · 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 · 2019
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Randomized smiles strings improve the quality of molecular generative models
Josep Arús-Pous, Simon Viet Johansson, Oleksii Prykhodko, Esben Jannik Bjerrum, Christian Tyrchan, Jean-Louis Reymond, Hongming Chen, and Ola Engkvist · 2019
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Mario Krenn, Florian Häse, AkshatKumar Nigam, Pascal Friederich, and Alán Aspuru-Guzik · 2019
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A de novo molecular generation method using latent vector based generative adversarial network
Oleksii Prykhodko, Simon Viet Johansson, Panagiotis-Christos Kotsias, Josep Arús-Pous, Esben Jannik Bjerrum, Ola Engkvist, and Hongming Chen · 2019
Closest in time.
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