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Machine learning for molecules holds great potential for efficiently exploring the vast chemical space and thus streamlining the drug discovery process by facilitating the design of new therapeutic molecules.
Scaffold based molecular design using graph generative model
Jaechang Lim, Sang-Yeon Hwang, Seungsu Kim, Seokhyun Moon, and Woo Youn Kim · 1905
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MolecularRNN: Generating realistic molecular graphs with optimized properties
Mariya Popova, Mykhailo Shvets, Junier Oliva, and Olexandr Isayev · 1905
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RDKit, 2010
G. Landrum · 2010
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Auto-encoding variational bayes
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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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Flow network based generative models for non-iterative diverse candidate generation
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Graph Networks for Molecular Design
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