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This work introduces a method to tune a sequence-based generative model for molecular de novo design that through augmented episodic likelihood can learn to generate structures with certain specified desirable properties.
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Hartenfeller, M., Zettl, H., Walter, M., Rupp, M., Reisen, F., Proschak, E., Weggen, S., Stark, H., Schneider, G.: Dogs: Reaction-driven de novo design of bioactive compounds. PLOS Computational Biology 8
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Segler, M.H.S., Kogej, T., Tyrchan, C., Waller, M.P.: Generating Focussed Molecule Libraries for Drug Discovery with Recurrent Neural Networks. ArXiv e-prints (2017). 1701.01329
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