Automatic chemical design using a data-driven continuous representation of molecules

7 October 2016

Rafael Gómez-Bombarelli

Jennifer N. Wei

David Duvenaud

José Miguel Hernández-Lobato

Benjamín Sánchez-Lengeling

Dennis Sheberla

J. Aguilera-Iparraguirre

Papers citing "Automatic chemical design using a data-driven continuous representation of molecules"

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Title
A Two-Step Graph Convolutional Decoder for Molecule Generation Xavier Bresson T. Laurent 17 60 0 08 Jun 2019
An Introduction to Variational Autoencoders Diederik P. Kingma Max Welling BDL SSL DRL 33 2,290 0 06 Jun 2019
Probabilistic hypergraph grammars for efficient molecular optimization E. Kraev Mark Harley 21 1 0 05 Jun 2019
Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules Niklas W. A. Gebauer M. Gastegger Kristof T. Schütt 35 201 0 02 Jun 2019
Self-Referencing Embedded Strings (SELFIES): A 100% robust molecular string representation Mario Krenn Florian Hase AkshatKumar Nigam Pascal Friederich Alán Aspuru-Guzik 13 70 0 31 May 2019
Scaffold-based molecular design using graph generative model Jaechang Lim Sang-Yeon Hwang Seungsu Kim Seokhyun Moon Woo Youn Kim 30 17 0 31 May 2019
Deep Bayesian Optimization on Attributed Graphs Jiaxu Cui Bo Yang Xia Hu 11 6 0 31 May 2019
MolecularRNN: Generating realistic molecular graphs with optimized properties Mariya Popova Mykhailo Shvets Junier Oliva Olexandr Isayev GNN 35 164 0 31 May 2019
All SMILES Variational Autoencoder Zaccary Alperstein Artem Cherkasov J. Rolfe DRL 19 38 0 30 May 2019
Efficient Amortised Bayesian Inference for Hierarchical and Nonlinear Dynamical Systems Geoffrey Roeder Paul K. Grant Andrew Phillips Neil Dalchau Edward Meeds 17 23 0 28 May 2019
Leveraging binding-site structure for drug discovery with point-cloud methods Vincent Mallet Carlos G. Oliver N. Moitessier J. Waldispühl 16 7 0 28 May 2019
GraphNVP: An Invertible Flow Model for Generating Molecular Graphs Kaushalya Madhawa Katushiko Ishiguro Kosuke Nakago Motoki Abe BDL 14 188 0 28 May 2019
Learning by stochastic serializations Pablo Strasser S. Armand Stéphane Marchand-Maillet Alexandros Kalousis 21 0 0 27 May 2019
Adversarial Learned Molecular Graph Inference and Generation Sebastian Polsterl Christian Wachinger GAN 30 7 0 24 May 2019
A COLD Approach to Generating Optimal Samples Omar Mahmood José Miguel Hernández-Lobato 11 8 0 23 May 2019
Accelerated Discovery of Sustainable Building Materials Xiou Ge Richard Goodwin J. Gregory R. Kirchain Joana Maria L. Varshney 8 6 0 20 May 2019
Affine Variational Autoencoders: An Efficient Approach for Improving Generalization and Robustness to Distribution Shift Rene Bidart A. Wong DRL OOD 11 5 0 13 May 2019
A Deep Generative Model for Graph Layout Oh-Hyun Kwon K. Ma DRL GNN 21 48 0 27 Apr 2019
D-VAE: A Variational Autoencoder for Directed Acyclic Graphs Muhan Zhang Shali Jiang Zhicheng Cui Roman Garnett Yixin Chen GNN BDL CML 32 196 0 24 Apr 2019
A survey on Big Data and Machine Learning for Chemistry J. F. Rodrigues L. Florea Maria Cristina Ferreira de Oliveira D. Diamond Osvaldo N. Oliveira AI4CE 11 6 0 23 Apr 2019
Decoding Molecular Graph Embeddings with Reinforcement Learning S. Kearnes Li Li Patrick F. Riley OffRL GNN 6 27 0 18 Apr 2019
Feature-Based Interpolation and Geodesics in the Latent Spaces of Generative Models Lukasz Struski M. Sadowski Tomasz Danel Jacek Tabor Igor T. Podolak DiffM 28 7 0 06 Apr 2019
Analyzing Learned Molecular Representations for Property Prediction Kevin Kaichuang Yang Kyle Swanson Wengong Jin Connor W. Coley Philipp Eiden ... Andrew Palmer Volker Settels Tommi Jaakkola K. Jensen Regina Barzilay 17 1,288 0 02 Apr 2019
From Variational to Deterministic Autoencoders Partha Ghosh Mehdi S. M. Sajjadi Antonio Vergari Michael J. Black Bernhard Schölkopf DRL 34 269 0 29 Mar 2019
Uncertainty quantification of molecular property prediction with Bayesian neural networks Seongok Ryu Yongchan Kwon W. Kim BDL 11 17 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 27 326 0 11 Mar 2019
High-dimensional Bayesian optimization using low-dimensional feature spaces Riccardo Moriconi M. Deisenroth K. S. S. Kumar 6 11 0 27 Feb 2019
Atomistic structure learning M. Jørgensen H. L. Mortensen S. A. Meldgaard E. L. Kolsbjerg Thomas L. Jacobsen K. H. Sørensen B. Hammer AI4CE 15 35 0 27 Feb 2019
Probabilistic Generative Deep Learning for Molecular Design Daniel T. Chang BDL AI4CE 47 7 0 11 Feb 2019
Clustering Bioactive Molecules in 3D Chemical Space with Unsupervised Deep Learning Chu Qin Ying Tan Tian Jin Xian Zeng Xingxing Qi ... Peng Zhang F. Zhu Hongping Zhao Yu Yang Jiang Yuzong Chen 26 0 0 09 Feb 2019
Mol-CycleGAN - a generative model for molecular optimization Łukasz Maziarka Agnieszka Pocha Jan Kaczmarczyk Krzysztof Rataj M. Warchoł 25 241 0 06 Feb 2019
Bayesian semi-supervised learning for uncertainty-calibrated prediction of molecular properties and active learning Yao Zhang A. Lee 19 101 0 03 Feb 2019
Learnable Embedding Space for Efficient Neural Architecture Compression Shengcao Cao Xiaofang Wang Kris M. Kitani 6 43 0 01 Feb 2019
Conditioning by adaptive sampling for robust design David H. Brookes Hahnbeom Park Jennifer Listgarten 19 193 0 29 Jan 2019
Deep Learning on Attributed Graphs: A Journey from Graphs to Their Embeddings and Back M. Simonovsky BDL GNN 29 1 0 24 Jan 2019
Conditional deep surrogate models for stochastic, high-dimensional, and multi-fidelity systems Yibo Yang P. Perdikaris SyDa BDL AI4CE 29 55 0 15 Jan 2019
A Comprehensive Survey on Graph Neural Networks Zonghan Wu Shirui Pan Fengwen Chen Guodong Long Chengqi Zhang Philip S. Yu FaML GNN AI4TS AI4CE 159 8,362 0 03 Jan 2019
Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks Edward J. Kim Z. Jensen Alexander van Grootel Kevin Huang Matthew Staib ... Haw-Shiuan Chang Emma Strubell Andrew McCallum Stefanie Jegelka E. Olivetti 14 115 0 31 Dec 2018
Drug cell line interaction prediction Pengfei Liu 23 114 0 28 Dec 2018
Learning Latent Subspaces in Variational Autoencoders Jack Klys Jake C. Snell R. Zemel SSL DRL 11 139 0 14 Dec 2018
Adversarial Autoencoders with Constant-Curvature Latent Manifolds Daniele Grattarola L. Livi Cesare Alippi BDL 11 27 0 11 Dec 2018
Coarse-Graining Auto-Encoders for Molecular Dynamics Wujie Wang Rafael Gómez-Bombarelli AI4CE 19 165 0 06 Dec 2018
MIWAE: Deep Generative Modelling and Imputation of Incomplete Data Pierre-Alexandre Mattei J. Frellsen SyDa 25 45 0 06 Dec 2018
Boltzmann Generators -- Sampling Equilibrium States of Many-Body Systems with Deep Learning Frank Noé Simon Olsson Jonas Köhler Hao Wu 18 32 0 04 Dec 2018
Learning Multimodal Graph-to-Graph Translation for Molecular Optimization Wengong Jin Kevin Kaichuang Yang Regina Barzilay Tommi Jaakkola 33 224 0 03 Dec 2018
Discovering Molecular Functional Groups Using Graph Convolutional Neural Networks Phillip E. Pope Soheil Kolouri Mohammad Rostami Charles E. Martin Heiko Hoffmann GNN 35 14 0 01 Dec 2018
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 197 638 0 29 Nov 2018
Grammars and reinforcement learning for molecule optimization E. Kraev 20 6 0 27 Nov 2018
DEFactor: Differentiable Edge Factorization-based Probabilistic Graph Generation Rim Assouel Mohamed Ahmed Marwin H. S. Segler Amir Saffari Yoshua Bengio 24 54 0 24 Nov 2018
GuacaMol: Benchmarking Models for De Novo Molecular Design Nathan Brown Marco Fiscato Marwin H. S. Segler Alain C. Vaucher ELM 44 692 0 22 Nov 2018