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Constrained Bayesian Optimization for Automatic Chemical Design

16 September 2017

Ryan-Rhys Griffiths

José Miguel Hernández-Lobato

BDL

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Papers citing "Constrained Bayesian Optimization for Automatic Chemical Design"

50 / 67 papers shown

Title
Decision-Making with Auto-Encoding Variational Bayes Romain Lopez Pierre Boyeau Nir Yosef Michael I. Jordan Jeffrey Regier BDL 491 10,591 0 17 Feb 2020
Multiple-objective Reinforcement Learning for Inverse Design and Identification Haoran Wei Mariefel V. Olarte Garrett B. Goh AI4CE 29 3 0 09 Oct 2019
ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations Ksenia Korovina Sailun Xu Kirthevasan Kandasamy Willie Neiswanger Barnabás Póczós J. Schneider Eric Xing 94 123 0 05 Aug 2019
Generative Models for Automatic Chemical Design Daniel Schwalbe-Koda Rafael Gómez-Bombarelli MedIm AI4CE 75 81 0 02 Jul 2019
A Model to Search for Synthesizable Molecules John Bradshaw Brooks Paige Matt J. Kusner Marwin H. S. Segler José Miguel Hernández-Lobato 59 109 0 12 Jun 2019
Bayesian Optimization of Composite Functions Raul Astudillo P. Frazier LLMAG 54 78 0 04 Jun 2019
Self-Referencing Embedded Strings (SELFIES): A 100% robust molecular string representation Mario Krenn Florian Hase AkshatKumar Nigam Pascal Friederich Alán Aspuru-Guzik 85 71 0 31 May 2019
MolecularRNN: Generating realistic molecular graphs with optimized properties Mariya Popova Mykhailo Shvets Junier Oliva Olexandr Isayev GNN 72 167 0 31 May 2019
Adversarial Learned Molecular Graph Inference and Generation Sebastian Polsterl Christian Wachinger GAN 104 7 0 24 May 2019
A COLD Approach to Generating Optimal Samples Omar Mahmood José Miguel Hernández-Lobato 38 8 0 23 May 2019
Uncertainty quantification of molecular property prediction with Bayesian neural networks Seongok Ryu Yongchan Kwon W. Kim BDL 44 18 0 20 Mar 2019
Deep learning for molecular design - a review of the state of the art Daniel C. Elton Zois Boukouvalas M. Fuge Peter W. Chung AI4CE 3DV 76 328 0 11 Mar 2019
High-dimensional Bayesian optimization using low-dimensional feature spaces Riccardo Moriconi M. Deisenroth K. S. S. Kumar 122 11 0 27 Feb 2019
Probabilistic Generative Deep Learning for Molecular Design Daniel T. Chang BDL AI4CE 63 7 0 11 Feb 2019
Mol-CycleGAN - a generative model for molecular optimization Łukasz Maziarka Agnieszka Pocha Jan Kaczmarczyk Krzysztof Rataj M. Warchoł 55 250 0 06 Feb 2019
Bayesian semi-supervised learning for uncertainty-calibrated prediction of molecular properties and active learning Yao Zhang A. Lee 46 103 0 03 Feb 2019
Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models Daniil Polykovskiy Alexander Zhebrak Benjamín Sánchez-Lengeling Sergey Golovanov Oktai Tatanov ... Simon Johansson Hongming Chen Sergey I. Nikolenko Alán Aspuru-Guzik Alex Zhavoronkov ELM 259 653 0 29 Nov 2018
Grammars and reinforcement learning for molecule optimization E. Kraev 44 6 0 27 Nov 2018
GuacaMol: Benchmarking Models for De Novo Molecular Design Nathan Brown Marco Fiscato Marwin H. S. Segler Alain C. Vaucher ELM 114 711 0 22 Nov 2018
Graph Convolutional Neural Networks for Polymers Property Prediction M. Zeng J. Kumar Zengfeng Zeng R. Savitha V. Chandrasekhar K. Hippalgaonkar GNN 59 29 0 15 Nov 2018
Molecular Transformer - A Model for Uncertainty-Calibrated Chemical Reaction Prediction P. Schwaller Teodoro Laino John McGuinness A. Horváth Constantine Bekas A. Lee 108 741 0 06 Nov 2018
Optimization of Molecules via Deep Reinforcement Learning Zhenpeng Zhou S. Kearnes Li Li R. Zare Patrick F. Riley AI4CE 103 542 0 19 Oct 2018
Deep Confidence: A Computationally Efficient Framework for Calculating Reliable Errors for Deep Neural Networks I. Cortés-Ciriano A. Bender OOD UQCV 60 61 0 24 Sep 2018
Molecular Hypergraph Grammar with its Application to Molecular Optimization Hiroshi Kajino 47 104 0 08 Sep 2018
Latent Molecular Optimization for Targeted Therapeutic Design Tristan Aumentado-Armstrong 39 41 0 05 Sep 2018
Improving Chemical Autoencoder Latent Space and Molecular De novo Generation Diversity with Heteroencoders E. Bjerrum Boris Sattarov BDL 42 149 0 25 Jun 2018
Molecular generative model based on conditional variational autoencoder for de novo molecular design Jaechang Lim Seongok Ryu Jin Woo Kim W. Kim BDL DRL 63 333 0 15 Jun 2018
Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation Jiaxuan You Bowen Liu Rex Ying Vijay S. Pande J. Leskovec GNN 295 902 0 07 Jun 2018
MolGAN: An implicit generative model for small molecular graphs Nicola De Cao Thomas Kipf GNN GAN 179 928 0 30 May 2018
Deeply learning molecular structure-property relationships using attention- and gate-augmented graph convolutional network Seongok Ryu Jaechang Lim S. Hong W. Kim GNN 36 67 0 28 May 2018
Conditional molecular design with deep generative models Seokho Kang Kyunghyun Cho BDL 226 183 0 30 Apr 2018
Learning Deep Generative Models of Graphs Yujia Li Oriol Vinyals Chris Dyer Razvan Pascanu Peter W. Battaglia GNN AI4CE 196 662 0 08 Mar 2018
Syntax-Directed Variational Autoencoder for Structured Data H. Dai Yingtao Tian Bo Dai Steven Skiena Le Song 102 329 0 24 Feb 2018
NeVAE: A Deep Generative Model for Molecular Graphs Bidisha Samanta A. De G. Jana P. Chattaraj Niloy Ganguly Manuel Gomez Rodriguez GNN DRL BDL DiffM 63 218 0 14 Feb 2018
Junction Tree Variational Autoencoder for Molecular Graph Generation Wengong Jin Regina Barzilay Tommi Jaakkola 352 1,369 0 12 Feb 2018
GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders M. Simonovsky N. Komodakis GNN BDL 120 852 0 09 Feb 2018
Multi-Objective De Novo Drug Design with Conditional Graph Generative Model Yibo Li L. Zhang Zhenming Liu 76 338 0 18 Jan 2018
In silico generation of novel, drug-like chemical matter using the LSTM neural network P. Ertl Richard A. Lewis E. Martin V. Polyakov 50 59 0 20 Dec 2017
Learning a Generative Model for Validity in Complex Discrete Structures David Janz J. Westhuizen Brooks Paige Matt J. Kusner José Miguel Hernández-Lobato 72 21 0 05 Dec 2017
Application of generative autoencoder in de novo molecular design T. Blaschke Marcus Olivecrona Ola Engkvist J. Bajorath Hongming Chen AI4CE 99 345 0 21 Nov 2017
"Found in Translation": Predicting Outcomes of Complex Organic Chemistry Reactions using Neural Sequence-to-Sequence Models P. Schwaller T. Gaudin D. Lanyi C. Bekas Teodoro Laino 74 281 0 13 Nov 2017
Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network Wengong Jin Connor W. Coley Regina Barzilay Tommi Jaakkola 74 277 0 13 Sep 2017
Actively Learning what makes a Discrete Sequence Valid David Janz J. Westhuizen José Miguel Hernández-Lobato 47 22 0 15 Aug 2017
Bayesian Optimization for Probabilistic Programs Tom Rainforth T. Le Jan-Willem van de Meent Michael A. Osborne Frank Wood TPM 39 26 0 13 Jul 2017
Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models G. L. Guimaraes Benjamín Sánchez-Lengeling Carlos Outeiral Pedro Luis Cunha Farias Alán Aspuru-Guzik GAN 82 525 0 30 May 2017
Molecular De Novo Design through Deep Reinforcement Learning Marcus Olivecrona T. Blaschke Ola Engkvist Hongming Chen BDL 130 1,017 0 25 Apr 2017
Grammar Variational Autoencoder Matt J. Kusner Brooks Paige José Miguel Hernández-Lobato BDL DRL 85 844 0 06 Mar 2017
Sequence Tutor: Conservative Fine-Tuning of Sequence Generation Models with KL-control Natasha Jaques S. Gu Dzmitry Bahdanau José Miguel Hernández-Lobato Richard Turner Douglas Eck 98 173 0 09 Nov 2016
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 J. Aguilera-Iparraguirre Timothy D. Hirzel Ryan P. Adams Alán Aspuru-Guzik 3DV 174 2,939 0 07 Oct 2016
WaveNet: A Generative Model for Raw Audio Aaron van den Oord Sander Dieleman Heiga Zen Karen Simonyan Oriol Vinyals Alex Graves Nal Kalchbrenner A. Senior Koray Kavukcuoglu DiffM 406 7,414 0 12 Sep 2016