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Deep generative models have been applied with increasing success to the generation of two dimensional molecules as SMILES strings and molecular graphs.
SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules
David Weininger · 1988
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Virtual high-throughput in silico screening
Markus H. J. Seifert, Kristina Wolf, and Daniel Vitt · 2003
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Quo vadis, virtual screening? a comprehensive survey of prospective applications
Peter Ripphausen, Britta Nisius, Lisa Peltason, and Jurgen Bajorath · 2010
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Philip N Collier, Gabriel Martinez-Botella, Mark Cornebise, Kevin M Cottrell, John D Doran, James P Griffith, Sudipta Mahajan, Francois Maltais, Cameron S Moody, Emilie Porter Huck, Tiansheng Wang, and Alex M Aronov · 2010
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Hit identification and optimization in virtual screening: practical recommendations based on a critical literature analysis: miniperspective
Tian Zhu, Shuyi Cao, Pin-Chih Su, Ram Patel, Darshan Shah, Heta B Chokshi, Richard Szukala, Michael E Johnson, and Kirk E Hevener · 2013
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Generative adversarial networks
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Semi-supervised Learning with Deep Generative Models
Diederik P Kingma, Danilo J Rezende, Shakir Mohamed, and Max Welling · 2014
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Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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Izhar Wallach, Michael Dzamba, and Abraham Heifets · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Lei Ba · 2015
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Deep learning for computational biology
Christof Angermueller, Tanel Pärnamaa, Leopold Parts, and Oliver Stegle · 2016
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Autoencoding beyond pixels using a learned similarity metric
Anders Boesen Lindbo Larsen, Søren Kaae Sønderby, Hugo Larochelle, and Ole Winther · 2016
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Unsupervised representation learning with deep convolutional generative adversarial networks
Soumith Chintala Alec Radford, Luke Metz · 2016
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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
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Protein-Ligand Scoring with Convolutional Neural Networks
Matthew Ragoza, Joshua Hochuli, Elisa Idrobo, Jocelyn Sunseri, and David Ryan Koes · 2017
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Improved Training of Wasserstein GANs
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron Courville · 2017
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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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Molecular generative model based on conditional variational autoencoder for de novo molecular design
Jaechang Lim, Seongok Ryu, Jin W Kim, and Woo Y Kim · 2018
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GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders
Martin Simonovsky and Nikos Komodakis · 2018
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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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Junction Tree Variational Autoencoder for Molecular Graph Generation
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Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Generating focussed 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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Molecular generation with recurrent neural networks (rnns)
Esben J Bjerrum and Richard Threlfall · 2017
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Grammar variational autoencoder
Matt J. Kusner, Brooks Paige, and José Miguel Hernández-Lobato · 2017
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Objective-reinforced generative adversarial networks (organ) for sequence generation models
Gabriel Guimaraes, Benjamin Sanchez-Lengeling, Carlos Outeiral, Pedro LC Farias, and Alán Aspuru-Guzik · 2017
Cited alongside, same era.
Optimizing distributions over molecular space. an objective-reinforced generative adversarial network for inverse-design chemistry (organic)
Benjamin Sanchez-Lengeling, Carlos Outeiral, Gabriel L Guimaraes, and Alán Aspuru-Guzik · 2017
Cited alongside, same era.
Protein family-specific models using deep neural networks and transfer learning improve virtual screening and highlight the need for more data
Fergus Imrie, Anthony R Bradley, Mihaela van der Schaar, and Charlotte M Deane · 2018
Cited alongside, same era.
KDEEP: Protein–Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks
José Jiménez, Miha Škalič, Gerard Martínez-Rosell, and Gianni De Fabritiis · 2018
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Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2019
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Shape-Based Generative Modeling for de Novo Drug Design
Miha Skalic, José Jiménez, Davide Sabbadin, and Gianni De Fabritiis · 2019
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Three-Dimensional Convolutional Neural Networks and a Cross-Docked Data Set for Structure-Based Drug Design
Paul G Francoeur, Tomohide Masuda, Jocelyn Sunseri, Andrew Jia, Richard B Iovanisci, Ian Snyder, and David R Koes · 2020
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Rosenet: Improving binding affinity prediction by leveraging molecular mechanics energies with an ensemble of 3d convolutional neural networks
Hussein Hassan-Harrirou, Ce Zhang, and Thomas Lemmin · 2020
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De novo generation of hit-like molecules from gene expression signatures using artificial intelligence
O Méndez-Lucio, B Baillif, D Clevert, D Rouquié, and J Wichard · 2020
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Self-referencing embedded strings (selfies): A 100representation
Mario Krenn, Florian Häse, AkshatKumar Nigam, Pascal Friederich, and Alán Aspuru-Guzik · 2020
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Optimol : Optimization of Binding Affinities in Chemical Space for Drug Discovery
Jacques Boitreaud, Vincent Mallet, Carlos Oliver, and Jerome Waldispühl · 2020
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MathDL: mathematical deep learning for D3R Grand Challenge 4
Duc Duy Nguyen, Kaifu Gao, Menglum Wang, and Guo-wei Wei · 2020
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libmolgrid: Graphics Processing Unit Accelerated Molecular Gridding for Deep Learning Applications
Jocelyn Sunseri and David R Koes · 2020
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Learning a continuous representation of 3d molecular structures with deep generative models
Matthew Ragoza, Tomohide Masuda, and David Ryan Koes · 2020
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