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Molecule generation is central to a variety of applications.
Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules
D. Weininger · 1988
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Smiles. 2. algorithm for generation of unique smiles notation
D. Weininger, A. Weininger, and J. L. Weininger · 1989
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Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences
M. W. Gardner and S. Dorling · 1998
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Chemical similarity searching
P. Willett, J. M. Barnard, and G. M. Downs · 1998
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Prediction of physicochemical parameters by atomic contributions
S. A. Wildman and G. M. Crippen · 1999
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An apriori-based algorithm for mining frequent substructures from graph data
A. Inokuchi, T. Washio, and H. Motoda · 2000
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Frequent subgraph discovery
M. Kuramochi and G. Karypis · 2001
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gspan: Graph-based substructure pattern mining
X. Yan and J. Han · 2002
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A quickstart in frequent structure mining can make a difference
S. Nijssen and J. N. Kok · 2004
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Another look at the data sparsity problem
B. Allison, D. Guthrie, and L. Guthrie · 2006
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Chemoinformatics: a textbook
J. Gasteiger and T. Engel · 2006
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970 million druglike small molecules for virtual screening in the chemical universe database GDB-13
L. C. Blum and J.-L. Reymond · 2009
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The rise of fragment-based drug discovery
C. W. Murray and D. C. Rees · 2009
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Second-generation de novo design: a view from a medicinal chemist perspective
A. Zaliani, K. Boda, T. Seidel, A. Herwig, C. H. Schwab, J. Gasteiger, H. Claußen, C. Lemmen, J. Degen, J. Pärn, et al · 2009
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Extended-connectivity fingerprints
D. Rogers and M. Hahn · 2010
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Mathematical theory of entropy
N. F. Martin and J. W. England · 2011
Cited alongside, same era.
Quantifying the chemical beauty of drugs
G. R. Bickerton, G. V. Paolini, J. Besnard, S. Muresan, and A. L. Hopkins · 2012
Cited alongside, same era.
Zinc: a free tool to discover chemistry for biology
J. J. Irwin, T. Sterling, M. M. Mysinger, E. S. Bolstad, and R. G. Coleman · 2012
Cited alongside, same era.
Fast and accurate modeling of molecular atomization energies with machine learning
M. Rupp, A. Tkatchenko, K.-R. Müller, and O. A. von Lilienfeld · 2012
Cited alongside, same era.
Auto-encoding variational bayes
D. P. Kingma and M. Welling · 2013
Cited alongside, same era.
Learning phrase representations using rnn encoder-decoder for statistical machine translation
Fréchet chemnet distance: a metric for generative models for molecules in drug discovery
K. Preuer, P. Renz, T. Unterthiner, S. Hochreiter, and G. Klambauer · 2018
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Graph convolutional policy network for goal-directed molecular graph generation
J. You, B. Liu, R. Ying, V. Pande, and J. Leskovec · 2018
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Guacamol: benchmarking models for de novo molecular design
N. Brown, M. Fiscato, M. H. Segler, and A. C. Vaucher · 2019
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Strategies for pre-training graph neural networks
W. Hu, B. Liu, J. Gomes, M. Zitnik, P. Liang, V. Pande, and J. Leskovec · 2019
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Efficient learning of non-autoregressive graph variational autoencoders for molecular graph generation
Y. Kwon, J. Yoo, Y.-S. Choi, W.-J. Son, D. Lee, and S. Kang · 2019
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K. Cho, B. Van Merriënboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio · 2014
Cited alongside, same era.
word2vec explained: deriving mikolov et al.’s negative-sampling word-embedding method
Y. Goldberg and O. Levy · 2014
Cited alongside, same era.
Neural machine translation of rare words with subword units
R. Sennrich, B. Haddow, and A. Birch · 2015
Cited alongside, same era.
The word entropy of natural languages
C. Bentz and D. Alikaniotis · 2016
Cited alongside, same era.
Molecular generation with recurrent neural networks (rnns)
E. J. Bjerrum and R. Threlfall · 2017
Cited alongside, same era.
Grammar variational autoencoder
M. J. Kusner, B. Paige, and J. M. Hernández-Lobato · 2017
Cited alongside, same era.
Molgan: An implicit generative model for small molecular graphs
N. De Cao and T. Kipf · 2018
Cited alongside, same era.
Chembl: towards direct deposition of bioassay data
D. Mendez, A. Gaulton, A. P. Bento, J. Chambers, M. De Veij, E. Félix, M. P. Magariños, J. F. Mosquera, P. Mutowo, M. Nowotka, et al · 2019
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Molecularrnn: Generating realistic molecular graphs with optimized properties
M. Popova, M. Shvets, J. Oliva, and O. Isayev · 2019
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Guiding deep molecular optimization with genetic exploration
S. Ahn, J. Kim, H. Lee, and J. Shin · 2020
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Augmenting genetic algorithms with deep neural networks for exploring the chemical space
A. Nigam, P. Friederich, M. Krenn, and A. Aspuru-Guzik · 2020
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Graphaf: a flow-based autoregressive model for molecular graph generation
C. Shi, M. Xu, Z. Zhu, W. Zhang, M. Zhang, and J. Tang · 2020
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Frequent subgraph mining algorithms in static and temporal graph-transaction settings: A survey
A. Jazayeri and C. Yang · 2021
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Graphdf: A discrete flow model for molecular graph generation
Y. Luo, K. Yan, and S. Ji · 2021
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Mars: Markov molecular sampling for multi-objective drug discovery
Y. Xie, C. Shi, H. Zhou, Y. Yang, W. Zhang, Y. Yu, and L. Li · 2021
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Hit and lead discovery with explorative rl and fragment-based molecule generation
S. Yang, D. Hwang, S. Lee, S. Ryu, and S. J. Hwang · 2021
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