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De novo molecular generation is an essential task for science discovery.
Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules
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Recap retrosynthetic combinatorial analysis procedure: a powerful new technique for identifying privileged molecular fragments with useful applications in combinatorial chemistry
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Frequent subgraph discovery
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Rdkit: Open-source cheminformatics, 2006
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Extended-connectivity fingerprints
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Principles of early drug discovery
James P Hughes, Stephen Rees, S Barrett Kalindjian, and Karen L Philpott · 2011
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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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Enumeration of 166 billion organic small molecules in the chemical universe database gdb-17
Lars Ruddigkeit, Ruud Van Deursen, Lorenz C Blum, and Jean-Louis Reymond · 2012
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Chuntao Jiang, Frans Coenen, and Michele Zito · 2013
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Samuel R Bowman, Luke Vilnis, Oriol Vinyals, Andrew M Dai, Rafal Jozefowicz, and Samy Bengio · 2015
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Neural machine translation of rare words with subword units
Rico Sennrich, Barry Haddow, and Alexandra Birch · 2015
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Grammar variational autoencoder
Matt J Kusner, Brooks Paige, and José Miguel Hernández-Lobato · 2017
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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
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Multi-objective de novo drug design with conditional graph generative model
Yibo Li, Liangren Zhang, and Zhenming Liu · 2018
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Constrained graph variational autoencoders for molecule design
Qi Liu, Miltiadis Allamanis, Marc Brockschmidt, and Alexander Gaunt · 2018
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Fréchet chemnet distance: a metric for generative models for molecules in drug discovery
Kristina Preuer, Philipp Renz, Thomas Unterthiner, Sepp Hochreiter, and Gunter Klambauer · 2018
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Inverse molecular design using machine learning: Generative models for matter engineering
Benjamin Sanchez-Lengeling and Alán Aspuru-Guzik · 2018
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Generating focused molecule libraries for drug discovery with recurrent neural networks
Molecule optimization by explainable evolution
Binghong Chen, Tianzhe Wang, Chengtao Li, Hanjun Dai, and Le Song · 2021
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A review for deep reinforcement learning in atari: Benchmarks, challenges, and solutions
Jiajun Fan · 2021
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Data-efficient graph grammar learning for molecular generation
Minghao Guo, Veronika Thost, Beichen Li, Payel Das, Jie Chen, and Wojciech Matusik · 2021
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Graphpiece: Efficiently generating high-quality molecular graph with substructures
Xiangzhe Kong, Zhixing Tan, and Yang Liu · 2021
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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 · 2021
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Marwin HS Segler, Thierry Kogej, Christian Tyrchan, and Mark P Waller · 2018
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Population-based de novo molecule generation, using grammatical evolution
Naruki Yoshikawa, Kei Terayama, Masato Sumita, Teruki Homma, Kenta Oono, and Koji Tsuda · 2018
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Graph convolutional policy network for goal-directed molecular graph generation
Jiaxuan You, Bowen Liu, Zhitao Ying, Vijay Pande, and Jure Leskovec · 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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Strategies for pre-training graph neural networks
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A graph-based genetic algorithm and generative model/monte carlo tree search for the exploration of chemical space
Jan H Jensen · 2019
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Chembl: towards direct deposition of bioassay data
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Graph networks for molecular design
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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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Motif-based graph self-supervised learning for molecular property prediction
Zaixi Zhang, Qi Liu, Hao Wang, Chengqiang Lu, and Chee-Kong Lee · 2021
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Generative models for molecular discovery: Recent advances and challenges
Camille Bilodeau, Wengong Jin, Tommi Jaakkola, Regina Barzilay, and Klavs F Jensen · 2022
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Generalized data distribution iteration
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Modeling diverse chemical reactions for single-step retrosynthesis via discrete latent variables
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Biogpt: generative pre-trained transformer for biomedical text generation and mining
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Smt-dta: Improving drug-target affinity prediction with semi-supervised multi-task training
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Learning protein representations via complete 3d graph networks
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Learning task-relevant representations for generalization via characteristic functions of reward sequence distributions
Rui Yang, Jie Wang, Zijie Geng, Mingxuan Ye, Shuiwang Ji, Bin Li, and Feng Wu · 2022
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LMC: Fast training of GNNs via subgraph sampling with provable convergence
Zhihao Shi, Xize Liang, and Jie Wang · 2023
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Learning cut selection for mixed-integer linear programming via hierarchical sequence model
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