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Automatic Chemical Design is a framework for generating novel molecules with optimized properties.
A model to search for synthesizable molecules
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Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings
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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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Understanding the difficulty of training deep feedforward neural networks
X. Glorot and Y. Bengio · 2010
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The harvard clean energy project: Large-scale computational screening and design of organic photovoltaics on the world community grid
J. Hachmann, R. Olivares-Amaya, S. Atahan-Evrenk, C. Amador-Bedolla, R. S. Sánchez-Carrera., A. Gold-Parker, L. Vogt, A. M. Brockway, and A. Aspuru-Guzik · 2011
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Quantifying the chemical beauty of drugs
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Zinc: a free tool to discover chemistry for biology
J. J. Irwin, T. Sterling, M. M. Mysinger, E. S. Bolstad, and R. G. Coleman · 2012
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Lead candidates for high-performance organic photovoltaics from high-throughput quantum chemistry - the harvard clean energy project
J. Hachmann, R. Olivares-Amaya, A. Jinich, A. L. Appleton, M. A. Blood-Forsythe, L. R. Seress, C. Roman-Salgado, K. Trepte, S. Atahan-Evrenk, S. Er, S. Shrestha, R. Mondal, A. Sokolov, Z. Bao, and A. Aspuru-Guzik · 2014
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Auto-encoding variational bayes
Diederik P. Kingma and Max Welling · 2014
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Semi-supervised learning with deep generative models
Diederik P Kingma, Shakir Mohamed, Danilo Jimenez Rezende, and Max Welling · 2014
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Bayesian optimization with unknown constraints
Michael A Gelbart, Jasper Snoek, and Ryan P Adams · 2014
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Adam: A method for stochastic optimization
D. Kingma and J. Ba · 2014
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Initialization of Bayesian Optimization Viewed as Part of a Larger Algorithm Portfolio
Marius Tudor Morar, Joshua Knowles, and Sandra Sampaio · 2014
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Deep neural nets as a method for quantitative structure–activity relationships
Junshui Ma, Robert P Sheridan, Andy Liaw, George E Dahl, and Vladimir Svetnik · 2015
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What is high-throughput virtual screening? a perspective from organic materials discovery
E. O. Pyzer-Knapp, C. Suh, R. Gómez-Bombarelli, J. Aguilera-Iparraguirre, and A. Aspuru-Guzik · 2015
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Generating sentences from a continuous space
Samuel R. Bowman, Luke Vilnis, Oriol Vinyals, Andrew M. Dai, Rafal Józefowicz, and Samy Bengio · 2015
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Constrained Bayesian Optimization and Applications
M. A. Gelbart · 2015
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Convolutional networks on graphs for learning molecular fingerprints
D. Duvenaud, D. Maclaurin, J. Aguilera-Iparraguirre, R. Gómez-Bombarelli, T. Hirzel, A. Aspuru-Guzik, and R. P. Adams · 2015
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Massively multitask networks for drug discovery
Bharath Ramsundar, Steven M. Kearnes, Patrick Riley, Dale Webster, David E. Konerding, and Vijay S. Pande · 2015
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Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach
R. Gómez-Bombarelli, J. Aguilera-Iparraguirre, T. D. Hirzel, D. Duvenaud, D. Maclaurin, M. A. Blood-Forsythe, H. S. Chae, M. Einzinger, D-G. Ha, T. Wu, and G. Markopoulos · 2016
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Rdkit: open-source cheminformatics software, 2016
G Landrum · 2016
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Bayesian optimization for probabilistic programs
Tom Rainforth, Tuan Anh Le, Jan-Willem van de Meent, Michael A Osborne, and Frank Wood · 2016
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A unifying framework for sparse gaussian process approximation using power expectation propagation
T. D. Bui, J. Yan, and R. E. Turner · 2016
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Black-box alpha divergence minimization
J. M. Hernández-Lobato, Y. Li, M. Rowland, T. Bui, D. Hernández-Lobato, and R. E. Turner · 2016
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Theano: A Python framework for fast computation of mathematical expressions
Theano Development Team · 2016
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Convolutional embedding of attributed molecular graphs for physical property prediction
Connor W Coley, Regina Barzilay, William H Green, Tommi S Jaakkola, and Klavs F Jensen · 2017
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Predicting organic reaction outcomes with weisfeiler-lehman network
Wengong Jin, Connor Coley, Regina Barzilay, and Tommi Jaakkola · 2017
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Generating focused molecule libraries for drug discovery with recurrent neural networks
Marwin HS Segler, Thierry Kogej, Christian Tyrchan, and Mark P Waller · 2017
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Application of generative autoencoder in de novo molecular design
Thomas Blaschke, Marcus Olivecrona, Ola Engkvist, Jürgen Bajorath, and Hongming Chen · 2017
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In silico generation of novel, drug-like chemical matter using the LSTM neural network
Peter Ertl, Richard Lewis, Eric J. Martin, and Valery Polyakov · 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 Lima Guimaraes, Benjamin Sanchez-Lengeling, Pedro Luis Cunha Farias, and Alán Aspuru-Guzik · 2017
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A quantitative uncertainty metric controls error in neural network-driven chemical discovery
Jon Paul Janet, Chenru Duan, Tzuhsiung Yang, Aditya Nandy, and Heather Kulik · 2019
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Uncertainty quantification of molecular property prediction with bayesian neural networks
Seongok Ryu, Yongchan Kwon, and Woo Youn Kim · 2019
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Méthodes d’apprentissage statistique pour le criblage virtuel de médicament
Benoit Playe · 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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De novo molecular design by combining deep autoencoder recurrent neural networks with generative topographic mapping
Boris Sattarov, Igor I Baskin, Dragos Horvath, Gilles Marcou, Esben Jannik Bjerrum, and Alexandre Varnek · 2019
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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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Molecular de-novo design through deep reinforcement learning
Marcus Olivecrona, Thomas Blaschke, Ola Engkvist, and Hongming Chen · 2017
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Model-based methods for continuous and discrete global optimization
Thomas Bartz-Beielstein and Martin Zaefferer · 2017
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Machine learning for organic cage property prediction
Lukas Turcani, Rebecca L Greenaway, and Kim E Jelfs · 2018
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Predicting adverse drug reactions through interpretable deep learning framework
Sanjoy Dey, Heng Luo, Achille Fokoue, Jianying Hu, and Ping Zhang · 2018
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Graph convolutional neural networks for polymers property prediction
Minggang Zeng, Jatin Nitin Kumar, Zeng Zeng, Ramasamy Savitha, Vijay Ramaseshan Chandrasekhar, and Kedar Hippalgaonkar · 2018
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Deep confidence: a computationally efficient framework for calculating reliable prediction errors for deep neural networks
Isidro Cortés-Ciriano and Andreas Bender · 2018
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Penalized variational autoencoder for molecular design
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Nevae: A deep generative model for molecular graphs
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Molecular hypergraph grammar with its application to molecular optimization
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Learning multimodal graph-to-graph translation for molecule optimization
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A two-step graph convolutional decoder for molecule generation
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Scaffold-based molecular design using graph generative model
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Likelihood-free inference and generation of molecular graphs
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Graphnvp: An invertible flow model for generating molecular graphs
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Optimization of molecules via deep reinforcement learning
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Multiple-objective reinforcement learning for inverse design and identification
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Deep reinforcement learning for multiparameter optimization in de novo drug design
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