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Molecular discovery has brought great benefits to the chemical industry.
The art and practice of structure-based drug design: a molecular modeling perspective
Regine S Bohacek, Colin McMartin, and Wayne C Guida · 1996
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The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2008
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Quantifying the chemical beauty of drugs
G Richard Bickerton, Gaia V Paolini, Jérémy Besnard, Sorel Muresan, and Andrew L Hopkins · 2012
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ZINC: a free tool to discover chemistry for biology
John J Irwin, Teague Sterling, Michael M Mysinger, Erin S Bolstad, and Ryan G Coleman · 2012
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On the properties of neural machine translation: Encoder-decoder approaches
Kyunghyun Cho · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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ZINC 15–ligand discovery for everyone
Teague Sterling and John J Irwin · 2015
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Order matters: Sequence to sequence for sets
Oriol Vinyals, Samy Bengio, and Manjunath Kudlur · 2015
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Low data drug discovery with one-shot learning
Han Altae-Tran, Bharath Ramsundar, Aneesh S Pappu, and Vijay Pande · 2017
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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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Molecular de-novo design through deep reinforcement learning
M. Olivecrona, T. Blaschke, O. Engkvist, and H. Chen · 2017
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The hamming ball sampler
Michalis K Titsias and Christopher Yau · 2017
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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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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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Junction tree variational autoencoder for molecular graph generation
Wengong Jin, Regina Barzilay, and Tommi S. Jaakkola · 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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Graph convolutional policy network for goal-directed molecular graph generation
Jiaxuan You et al · 2018
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Guacamol: benchmarking models for de novo molecular design
Nathan Brown, Marco Fiscato, Marwin HS Segler, and Alain C Vaucher · 2019
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MIMOSA: Multi-constraint molecule sampling for molecule optimization
Tianfan Fu, Cao Xiao, Xinhao Li, Lucas M Glass, and Jimeng Sun · 2021
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Oops i took a gradient: Scalable sampling for discrete distributions
Will Grathwohl, Kevin Swersky, Milad Hashemi, David Duvenaud, and Chris Maddison · 2021
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Therapeutics data commons: machine learning datasets and tasks for therapeutics
Kexin Huang, Tianfan Fu, Wenhao Gao, Yue Zhao, Yusuf Roohani, Jure Leskovec, Connor W Coley, Cao Xiao, Jimeng Sun, and Marinka Zitnik · 2021
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Graphebm: Molecular graph generation with energy-based models
Meng Liu, Keqiang Yan, Bora Oztekin, and Shuiwang Ji · 2021
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MARS: Markov molecular sampling for multi-objective drug discovery
Yutong Xie, Chence Shi, Hao Zhou, Yuwei Yang, Weinan Zhang, Yong Yu, and Lei Li · 2021
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Integrated identification of disease specific pathways using multi-omics data
Yi-Tan Chang, Eric P Hoffman, Guoqiang Yu, David M Herrington, Robert Clarke, Chiung-Ting Wu, Lulu Chen, and Yue Wang · 2019
Cited alongside, same era.
Graph residual flow for molecular graph generation
Shion Honda, Hirotaka Akita, Katsuhiko Ishiguro, Toshiki Nakanishi, and Kenta Oono · 2019
Cited alongside, same era.
GraphNVP: An invertible flow model for generating molecular graphs
Kaushalya Madhawa et al · 2019
Cited alongside, same era.
Optimization of molecules via deep reinforcement learning
Zhenpeng Zhou, Steven Kearnes, Li Li, Richard N Zare, and Patrick Riley · 2019
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CORE: Automatic molecule optimization using copy and refine strategy
Tianfan Fu, Cao Xiao, and Jimeng Sun · 2020
Cited alongside, same era.
Learning to navigate the synthetically accessible chemical space using reinforcement learning
Sai Krishna Gottipati, Boris Sattarov, Sufeng Niu, Yashaswi Pathak, Haoran Wei, Shengchao Liu, Simon Blackburn, Karam Thomas, Connor Coley, Jian Tang, et al · 2020
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Multi-objective molecule generation using interpretable substructures
Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2020
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Antibody Complementarity Determining Regions (CDRs) design using constrained energy model
Tianfan Fu and Jimeng Sun · 2022
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Artificial intelligence foundation for therapeutic science
Kexin Huang, Tianfan Fu, Wenhao Gao, Yue Zhao, Yusuf Roohani, Jure Leskovec, Connor W Coley, Cao Xiao, Jimeng Sun, and Marinka Zitnik · 2022
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Optimal scaling for locally balanced proposals in discrete spaces
Haoran Sun, Hanjun Dai, and Dale Schuurmans · 2022
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A langevin-like sampler for discrete distributions
Ruqi Zhang, Xingchao Liu, and Qiang Liu · 2022
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Prior-preconditioned conjugate gradient method for accelerated gibbs sampling in “large n, large p” bayesian sparse regression
Akihiko Nishimura and Marc A Suchard · 2023
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Genetic algorithms are strong baselines for molecule generation, 2023
Austin Tripp and José Miguel Hernández-Lobato · 2023
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Genetic-guided gflownets for sample efficient molecular optimization, 2024
Hyeonah Kim, Minsu Kim, Sanghyeok Choi, and Jinkyoo Park · 2024
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Gradient-based discrete sampling with automatic cyclical scheduling
Patrick Pynadath, Riddhiman Bhattacharya, Arun Hariharan, and Ruqi Zhang · 2024
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