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ACS Central Science
We report a method to convert discrete representations of molecules to and from a multidimensional continuous representation. This model allows us to generate new molecules for efficient exploration and optimization through open-ended spaces of chemical compounds. A deep neural network was trained on hundreds of thousands of existing chemical structures to construct three coupled functions: an encoder, a decoder and a predictor. The encoder converts the discrete representation of a moleculedoi:10.1021/acscentsci.7b00572 pmid:29532027 pmcid:PMC5833007 fatcat:eun57eul2vcpjfikuaowor3wtu