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In recent years, the prediction of quantum mechanical observables withmachine learning methods has become increasingly popular. Message-passingneural networks (MPNNs) solve this task by constructing atomic representations,from which the properties of interest are predicted. Here, we introduce amethod to automatically identify chemical moieties (molecular building blocks)from such representations, enabling a variety of applications beyond propertyprediction, which otherwise rely on expert knowledge. The requiredrepresentation can either be provided by a pretrained MPNN, or learned fromscratch using only structural information. Beyond the data-driven design ofmolecular fingerprints, the versatility of our approach is demonstrated byenabling the selection of representative entries in chemical databases, theautomatic construction of coarse-grained force fields, as well as theidentification of reaction coordinates.

Automatic Identification of Chemical Moieties