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Deep generative models are able to suggest new organic molecules by generating strings, trees, and graphs representing their structure.
The generation of a unique machine description for chemical structures-a technique developed at chemical abstracts service
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Scubidoo: A large yet screenable and easily searchable database of computationally created chemical compounds optimized toward high likelihood of synthetic tractability
Florent Chevillard and Peter Kolb · 2015
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Convolutional networks on graphs for learning molecular fingerprints
David K Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarell, Timothy Hirzel, Alán Aspuru-Guzik, and Ryan P Adams · 2015
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What is high-throughput virtual screening? A perspective from organic materials discovery
Edward O Pyzer-Knapp, Changwon Suh, Rafael Gómez-Bombarelli, Jorge Aguilera-Iparraguirre, and Alán Aspuru-Guzik · 2015
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Learning deep generative models of graphs
Yujia Li, Oriol Vinyals, Chris Dyer, Razvan Pascanu, and Peter Battaglia · 2018
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Constrained graph variational autoencoders for molecule design
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Large-scale comparison of machine learning methods for drug target prediction on chembl
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Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models
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Gabriel Lima Guimaraes, Benjamin Sanchez-Lengeling, Carlos Outeiral, Pedro Luis Cunha Farias, and Alán Aspuru-Guzik · 2017
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Grammar variational autoencoder
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RDKit: Open-source cheminformatics
RDKit, online · 2018
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Planning chemical syntheses with deep neural networks and symbolic AI
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GraphVAE: Towards generation of small graphs using variational autoencoders
Martin Simonovsky and Nikos Komodakis · 2018
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Ilya Tolstikhin, Olivier Bousquet, Sylvain Gelly, and Bernhard Schoelkopf · 2018
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Graph convolutional policy network for goal-directed molecular graph generation
Jiaxuan You, Bowen Liu, Rex Ying, Vijay Pande, and Jure Leskovec · 2018
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A generative model for electron paths
John Bradshaw, Matt J Kusner, Brooks Paige, Marwin HS Segler, and José Miguel Hernández-Lobato · 2019
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Virtual compound libraries in computer-assisted drug discovery
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