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Recent advances in machine learning for molecules exhibit great potential for facilitating drug discovery from in silico predictions.
RDKit, 2010
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Variational Dropout and the Local Reparameterization Trick
Durk P Kingma, Tim Salimans, and Max Welling · 2015
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
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A next generation connectivity map: L1000 platform and the first 1,000,000 profiles
Aravind Subramanian, Rajiv Narayan, Steven M Corsello, David D Peck, Ted E Natoli, Xiaodong Lu, Joshua Gould, John F Davis, Andrew A Tubelli, Jacob K Asiedu, et al · 2017
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DEFactor: Differentiable Edge Factorization-based Probabilistic Graph Generation
Rim Assouel, Mohamed Ahmed, Marwin H. Segler, Amir Saffari, and Yoshua Bengio · 2018
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Machine learning for molecular and materials science
Keith T. Butler, Daniel W. Davies, Hugh Cartwright, Olexandr Isayev, and Aron Walsh · 2018
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MolGAN: An implicit generative model for small molecular graphs
Nicola De Cao and Thomas Kipf · 2018
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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, Jorge Aguilera-Iparraguirre, Timothy D Hirzel, Ryan P Adams, and Alán Aspuru-Guzik · 2018
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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, Jorge Aguilera-Iparraguirre, Timothy D. Hirzel, Ryan P. Adams, and Alán Aspuru-Guzik · 2018
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Junction Tree Variational Autoencoder for Molecular Graph Generation
Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2018
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Learning Deep Generative Models of Graphs
Yujia Li, Oriol Vinyals, Chris Dyer, Razvan Pascanu, and Peter Battaglia · 2018
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Constrained Graph Variational Autoencoders for Molecule Design
Qi Liu, Miltiadis Allamanis, Marc Brockschmidt, and Alexander L. Gaunt · 2018
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Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders
Tengfei Ma, Jie Chen, and Cao Xiao · 2018
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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 · 2018
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GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders, 2018
Martin Simonovsky and Nikos Komodakis · 2018
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MoleculeNet: A Benchmark for Molecular Machine Learning, 2018
Zhenqin Wu, Bharath Ramsundar, Evan N. Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S. Pappu, Karl Leswing, and Vijay Pande · 2018
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A Two Step Graph Convolutional Decoder for Molecule Generation
Xavier Bresson and Thomas Laurent · 2019
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GuacaMol: Benchmarking Models for de Novo Molecular Design
Nathan Brown, Marco Fiscato, Marwin H.S. Segler, and Alain C. Vaucher · 2019
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Molecular Hypergraph Grammar with Its Application to Molecular Optimization
Hiroshi Kajino · 2019
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Chembl: towards direct deposition of bioassay data
David Mendez, Anna Gaulton, A Patrícia Bento, Jon Chambers, Marleen De Veij, Eloy Félix, María Paula Magariños, Juan F Mosquera, Prudence Mutowo, Michał Nowotka, et al · 2019
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MolecularRNN: Generating realistic molecular graphs with optimized properties
Mariya Popova, Mykhailo Shvets, Junier Oliva, and Olexandr Isayev · 2019
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NeVAE: A Deep Generative Model for Molecular Graphs, 2019
Bidisha Samanta, Abir De, Gourhari Jana, Pratim Kumar Chattaraj, Niloy Ganguly, and Manuel Gomez-Rodriguez · 2019
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Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation
Jiaxuan You, Bowen Liu, Rex Ying, Vijay Pande, and Jure Leskovec · 2019
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Graph Deconvolutional Generation
Graph Networks for Molecular Design
Rocío Mercado, Tobias Rastemo, Edvard Lindelöf, Günter Klambauer, Ola Engkvist, Hongming Chen, and Esben Jannik Bjerrum · 2021
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Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification, 2021
Yunsheng Shi, Zhengjie Huang, Shikun Feng, Hui Zhong, Wenjin Wang, and Yu Sun · 2021
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Hit and Lead Discovery with Explorative RL and Fragment-based Molecule Generation
Soojung Yang, Doyeong Hwang, Seul Lee, Seongok Ryu, and Sung Ju Hwang · 2021
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De novo molecular generation via connection-aware motif mining
Zijie Geng, Shufang Xie, Yingce Xia, Lijun Wu, Tao Qin, Jie Wang, Yongdong Zhang, Feng Wu, and Tie-Yan Liu · 2022
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Predicting cellular responses to novel drug perturbations at a single-cell resolution
Leon Hetzel, Simon Böhm, Niki Kilbertus, Stephan Günnemann, Mohammad Lotfollahi, and Fabian Theis · 2022
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Daniel Flam-Shepherd, Tony Wu, and Alan Aspuru-Guzik · 2020
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ZINC20—A Free Ultralarge-Scale Chemical Database for Ligand Discovery
John J. Irwin, Khanh G. Tang, Jennifer Young, Chinzorig Dandarchuluun, Benjamin R. Wong, Munkhzul Khurelbaatar, Yurii S. Moroz, John Mayfield, and Roger A. Sayle · 2020
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Hierarchical Generation of Molecular Graphs using Structural Motifs
Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2020
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DeepGraphMolGen, a multi-objective, computational strategy for generating molecules with desirable properties: a graph convolution and reinforcement learning approach
Yash Khemchandani, Stephen O’Hagan, Soumitra Samanta, Neil Swainston, Timothy J. Roberts, Danushka Bollegala, and Douglas B. Kell · 2020
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Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models, 2020
Daniil Polykovskiy, Alexander Zhebrak, Benjamin Sanchez-Lengeling, Sergey Golovanov, Oktai Tatanov, Stanislav Belyaev, Rauf Kurbanov, Aleksey Artamonov, Vladimir Aladinskiy, Mark Veselov, Artur Kadurin, Simon Johansson, Hongming Chen, Sergey Nikolenko, Alan Aspuru-Guzik, and Alex Zhavoronkov · 2020
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GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation
Chence Shi, Minkai Xu, Zhaocheng Zhu, Weinan Zhang, Ming Zhang, and Jian Tang · 2020
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MoFlow: An Invertible Flow Model for Generating Molecular Graphs
Chengxi Zang and Fei Wang · 2020
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Optimizing molecules using efficient queries from property evaluations
Samuel C. Hoffman, Vijil Chenthamarakshan, Kahini Wadhawan, Pin-Yu Chen, and Payel Das · 2022
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Molecule Generation by Principal Subgraph Mining and Assembling, 2022
Xiangzhe Kong, Wenbing Huang, Zhixing Tan, and Yang Liu · 2022
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Learning to Extend Molecular Scaffolds with Structural Motifs
Krzysztof Maziarz, Henry Jackson-Flux, Pashmina Cameron, Finton Sirockin, Nadine Schneider, Nikolaus Stiefl, Marwin Segler, and Marc Brockschmidt · 2022
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TD-GEN: Graph Generation Using Tree Decomposition
Hamed Shirzad, Hossein Hajimirsadeghi, Amir H. Abdi, and Greg Mori · 2022
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Molecular Language Model as Multi-task Generator, 2023
Yin Fang, Ningyu Zhang, Zhuo Chen, Xiaohui Fan, and Huajun Chen · 2023
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Chemical language models for de novo drug design: Challenges and opportunities
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The Power of Motifs as Inductive Bias for Learning Molecular Distributions
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Improving and generalizing flow-based generative models with minibatch optimal transport
Alexander Tong, Nikolay Malkin, Guillaume Huguet, Yanlei Zhang, Jarrid Rector-Brooks, Kilian Fatras, Guy Wolf, and Yoshua Bengio · 2023
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Language models can learn complex molecular distributions
Daniel Flam-Shepherd, Kevin Zhu, and Alán Aspuru-Guzik · 2041
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Scaffold based molecular design using graph generative model
Jaechang Lim, Sang-Yeon Hwang, Seungsu Kim, Seokhyun Moon, and Woo Youn Kim · 2041
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Leveraging molecular structure and bioactivity with chemical language models for de novo drug design
Michael Moret, Irene Pachon Angona, Leandro Cotos, Shen Yan, Kenneth Atz, Cyrill Brunner, Martin Baumgartner, Francesca Grisoni, and Gisbert Schneider · 2041
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Optimization of Molecules via Deep Reinforcement Learning
Zhenpeng Zhou, Steven Kearnes, Li Li, Richard N. Zare, and Patrick Riley · 2045
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Recent advances and applications of deep learning methods in materials science
Kamal Choudhary, Brian DeCost, Chi Chen, Anubhav Jain, Francesca Tavazza, Ryan Cohn, Cheol Woo Park, Alok Choudhary, Ankit Agrawal, Simon J. L. Billinge, Elizabeth Holm, Shyue Ping Ong, and Chris Wolverton · 2057
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