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Deep generative models have been shown powerful in generating novel molecules with desired chemical properties via their representations such as strings, trees or graphs.
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Yujia Li, Oriol Vinyals, Chris Dyer, Razvan Pascanu, and Peter Battaglia · 2018
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A model to search for synthesizable molecules
John Bradshaw, Brooks Paige, Matt J Kusner, Marwin HS Segler, and José Miguel Hernández-Lobato · 2019
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Nathan Brown, Marco Fiscato, Marwin HS Segler, and Alain C Vaucher · 2019
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Molecular transformer: a model for uncertainty-calibrated chemical reaction prediction
Philippe Schwaller, Teodoro Laino, Théophile Gaudin, Peter Bolgar, Christopher A Hunter, Costas Bekas, and Alpha A Lee · 2019
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Barking up the right tree: an approach to search over molecule synthesis dags
John Bradshaw, Brooks Paige, Matt J Kusner, Marwin HS Segler, and José Miguel Hernández-Lobato · 2020
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Retro*: learning retrosynthetic planning with neural guided a* search
Binghong Chen, Chengtao Li, Hanjun Dai, and Le Song · 2020
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Constrained graph variational autoencoders for molecule design
Qi Liu, Miltiadis Allamanis, Marc Brockschmidt, and Alexander L Gaunt · 2018
Cited alongside, same era.
Fréchet chemnet distance: a metric for generative models for molecules in drug discovery
Kristina Preuer, Philipp Renz, Thomas Unterthiner, Sepp Hochreiter, and Günter Klambauer · 2018
Cited alongside, same era.
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Machine learned prediction of reaction template applicability for data-driven retrosynthetic predictions of energetic materials
Michael E Fortunato, Connor W Coley, Brian C Barnes, and Klavs F Jensen · 2020
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Compret: a comprehensive recommendation framework for chemical synthesis planning with algorithmic enumeration
Ryosuke Shibukawa, Shoichi Ishida, Kazuki Yoshizoe, Kunihiro Wasa, Kiyosei Takasu, Yasushi Okuno, Kei Terayama, and Koji Tsuda · 2020
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