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Advancements in neural machinery have led to a wide range of algorithmic solutions for molecular property prediction. Two classes of models in particular have yielded promising results: neural networks applied to computed molecular fingerprints or expert-crafted descriptors and graph convolutional neural networks that construct a learned molecular representation by operating on the graph structure of the molecule. However, recent literature has yet to clearly determine which of these twodoi:10.1021/acs.jcim.9b00237 pmid:31361484 pmcid:PMC6727618 fatcat:lvudvf2wvzedfehqzova2gn4u4