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Adams","Alán Aspuru-Guzik"],"abstract":"We report a method to convert discrete representations of molecules to and\nfrom a multidimensional continuous representation. This model allows us to\ngenerate new molecules for efficient exploration and optimization through\nopen-ended spaces of chemical compounds. A deep neural network was trained on\nhundreds of thousands of existing chemical structures to construct three\ncoupled functions: an encoder, a decoder and a predictor. The encoder converts\nthe discrete representation of a molecule into a real-valued continuous vector,\nand the decoder converts these continuous vectors back to discrete molecular\nrepresentations. The predictor estimates chemical properties from the latent\ncontinuous vector representation of the molecule. 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