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
TITAN: T Cell Receptor Specificity Prediction with Bimodal Attention Networks Anna Weber Jannis Born María Rodríguez Martínez 19 129 0 21 Apr 2021
Permutation-Invariant Variational Autoencoder for Graph-Level Representation Learning R. Winter Frank Noé Djork-Arné Clevert BDL SSL 22 24 0 20 Apr 2021
Crystal structure prediction of materials with high symmetry using differential evolution Wenhui Yang Edirisuriya M Dilanga Siriwardane Rongzhi Dong Yuxin Li Jianjun Hu 224 16 0 20 Apr 2021
Learning to design drug-like molecules in three-dimensional space using deep generative models Yibo Li Jianfeng Pei L. Lai DiffM 37 111 0 17 Apr 2021
A Graph VAE and Graph Transformer Approach to Generating Molecular Graphs Joshua Mitton H. Senn Klaas Wynne Roderick Murray-Smith ViT GNN 13 22 0 09 Apr 2021
Creativity and Machine Learning: A Survey Giorgio Franceschelli Mirco Musolesi VLM AI4CE 34 40 0 06 Apr 2021
Neighbor Embedding Variational Autoencoder Renfei Tu Yang Liu Yongzeng Xue Cheng Wang Maozu Guo BDL DRL 17 0 0 21 Mar 2021
Using Molecular Embeddings in QSAR Modeling: Does it Make a Difference? María Virginia Sabando I. Ponzoni E. Milios Axel J. Soto 33 26 0 20 Mar 2021
MARS: Markov Molecular Sampling for Multi-objective Drug Discovery Yutong Xie Chence Shi Hao Zhou Yuwei Yang Weinan Zhang Yong Yu Lei Li 30 138 0 18 Mar 2021
Scaffold Embeddings: Learning the Structure Spanned by Chemical Fragments, Scaffolds and Compounds Austin R. Clyde A. Ramanathan Rick L. Stevens 30 4 0 11 Mar 2021
Learning to Extend Molecular Scaffolds with Structural Motifs Krzysztof Maziarz Henry Jackson-Flux Pashmina Cameron F. Sirockin Nadine Schneider N. Stiefl Marwin H. S. Segler Marc Brockschmidt AI4CE 29 69 0 05 Mar 2021
Golem: An algorithm for robust experiment and process optimization Matteo Aldeghi Florian Hase Riley J. Hickman Isaac Tamblyn Alán Aspuru-Guzik 29 22 0 05 Mar 2021
Generative Chemical Transformer: Neural Machine Learning of Molecular Geometric Structures from Chemical Language via Attention Hyunseung Kim Jonggeol Na Won Bo Lee 22 46 0 27 Feb 2021
NOMU: Neural Optimization-based Model Uncertainty Jakob Heiss Jakob Weissteiner Hanna Wutte Sven Seuken Josef Teichmann BDL 37 19 0 26 Feb 2021
Assigning Confidence to Molecular Property Prediction AkshatKumar Nigam R. Pollice Matthew F. D. Hurley Riley J. Hickman Matteo Aldeghi Naruki Yoshikawa Seyone Chithrananda Vincent A. Voelz Alán Aspuru-Guzik AI4CE 30 46 0 23 Feb 2021
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development Kexin Huang Tianfan Fu Wenhao Gao Yue Zhao Yusuf Roohani J. Leskovec Connor W. Coley Cao Xiao Jimeng Sun Marinka Zitnik OOD LM&MA 38 264 0 18 Feb 2021
BORE: Bayesian Optimization by Density-Ratio Estimation Louis C. Tiao Aaron Klein Matthias Seeger Edwin V. Bonilla Cédric Archambeau F. Ramos 35 26 0 17 Feb 2021
Artificial Intelligence based Autonomous Molecular Design for Medical Therapeutic: A Perspective R. P. Joshi Neeraj Kumar 16 2 0 10 Feb 2021
Graph Energy-based Model for Substructure Preserving Molecular Design Ryuichiro Hataya Hideki Nakayama Kazuki Yoshizoe 51 6 0 09 Feb 2021
GIBBON: General-purpose Information-Based Bayesian OptimisatioN Henry B. Moss David S. Leslie Javier I. González Paul Rayson 26 43 0 05 Feb 2021
MolGrow: A Graph Normalizing Flow for Hierarchical Molecular Generation Maksim Kuznetsov Daniil Polykovskiy 35 40 0 03 Feb 2021
GraphDF: A Discrete Flow Model for Molecular Graph Generation Youzhi Luo Keqiang Yan Shuiwang Ji DRL 185 188 0 01 Feb 2021
Neural representation and generation for RNA secondary structures Zichao Yan William L. Hamilton Mathieu Blanchette 40 2 0 01 Feb 2021
A reproducibility study of "Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space" Kevin Maik Jablonka Fergus Mcilwaine S. Garcia B. Smit Brian Yoo 12 3 0 01 Feb 2021
GraphEBM: Molecular Graph Generation with Energy-Based Models Meng Liu Keqiang Yan Bora Oztekin Shuiwang Ji 24 85 0 31 Jan 2021
CobBO: Coordinate Backoff Bayesian Optimization with Two-Stage Kernels Jian Tan Niv Nayman Mengchang Wang 21 4 0 13 Jan 2021
Auto-Encoding Molecular Conformations R. Winter Frank Noé Djork-Arné Clevert AI4CE 49 12 0 05 Jan 2021
Advances in Electron Microscopy with Deep Learning Jeffrey M. Ede 40 2 0 04 Jan 2021
Deep Graph Generators: A Survey Faezeh Faez Yassaman Ommi M. Baghshah Hamid R. Rabiee GNN AI4CE 53 57 0 31 Dec 2020
Good practices for Bayesian Optimization of high dimensional structured spaces E. Siivola Javier I. González Andrei Paleyes Aki Vehtari 30 37 0 31 Dec 2020
Deep Evolutionary Learning for Molecular Design Yifeng Li H. K. Ooi A. Tchagang 27 14 0 28 Dec 2020
Barking up the right tree: an approach to search over molecule synthesis DAGs John Bradshaw Brooks Paige Matt J. Kusner Marwin H. S. Segler José Miguel Hernández-Lobato 56 56 0 21 Dec 2020
Learn molecular representations from large-scale unlabeled molecules for drug discovery Pengyong Li Jun Wang Yixuan Qiao Hao Chen Yihuan Yu Xiaojun Yao Peng Gao Guotong Xie Sen Song GNN 33 28 0 21 Dec 2020
BERTChem-DDI : Improved Drug-Drug Interaction Prediction from text using Chemical Structure Information Ishani Mondal 11 8 0 21 Dec 2020
EVA: Generating Longitudinal Electronic Health Records Using Conditional Variational Autoencoders Siddharth Biswal S. Ghosh J. Duke B. Malin Walter F. Stewart Jimeng Sun SyDa BDL DRL 53 40 0 18 Dec 2020
Curiosity in exploring chemical space: Intrinsic rewards for deep molecular reinforcement learning Luca Thiede Mario Krenn AkshatKumar Nigam Alán Aspuru-Guzik 19 31 0 17 Dec 2020
Deep Molecular Dreaming: Inverse machine learning for de-novo molecular design and interpretability with surjective representations Cynthia X Shen Mario Krenn S. Eppel Alán Aspuru-Guzik 18 43 0 17 Dec 2020
Are we Forgetting about Compositional Optimisers in Bayesian Optimisation? Antoine Grosnit Alexander I. Cowen-Rivers Rasul Tutunov Ryan-Rhys Griffiths Jun Wang Haitham Bou-Ammar 19 13 0 15 Dec 2020
Mercer Features for Efficient Combinatorial Bayesian Optimization Aryan Deshwal Syrine Belakaria J. Doppa 21 32 0 14 Dec 2020
Molecular graph generation with Graph Neural Networks P. Bongini Monica Bianchini F. Scarselli GNN 30 136 0 14 Dec 2020
Optimizing Discrete Spaces via Expensive Evaluations: A Learning to Search Framework Aryan Deshwal Syrine Belakaria J. Doppa Alan Fern 22 22 0 14 Dec 2020
A Deep Generative Model for Molecule Optimization via One Fragment Modification Ziqi Chen Martin Renqiang Min Srinivasan Parthasarathy Xia Ning 21 62 0 08 Dec 2020
Creativity of Deep Learning: Conceptualization and Assessment Marcus Basalla Johannes Schneider Jan vom Brocke 31 14 0 03 Dec 2020
Latent Programmer: Discrete Latent Codes for Program Synthesis Joey Hong David Dohan Rishabh Singh Charles Sutton Manzil Zaheer 27 21 0 01 Dec 2020
Symmetry-Aware Actor-Critic for 3D Molecular Design G. Simm Robert Pinsler Gábor Csányi José Miguel Hernández-Lobato AI4CE 31 64 0 25 Nov 2020
Comparison of Atom Representations in Graph Neural Networks for Molecular Property Prediction Agnieszka Pocha Tomasz Danel Lukasz Maziarka GNN 32 7 0 23 Nov 2020
Reinforced Molecular Optimization with Neighborhood-Controlled Grammars Chenchen Xu Qiao Liu Minlie Huang Tao Jiang GNN OffRL 8 19 0 14 Nov 2020
Universal Activation Function For Machine Learning Brosnan Yuen Minh Tu Hoang Xiaodai Dong Tao Lu 26 40 0 07 Nov 2020
Polymers for Extreme Conditions Designed Using Syntax-Directed Variational Autoencoders Rohit Batra H. Dai T. D. Huan Lihua Chen Chiho Kim Will R. Gutekunst Le Song R. Ramprasad 6 67 0 04 Nov 2020
Bayesian Variational Optimization for Combinatorial Spaces Tony C Wu Daniel Flam-Shepherd Alán Aspuru-Guzik BDL 27 4 0 03 Nov 2020