Automatic Identification of Chemical Moieties

• URL: http://arxiv.org/abs/2203.16205v2
• Date: Thu, 27 Apr 2023 10:59:56 GMT
• Title: Automatic Identification of Chemical Moieties
• Authors: Jonas Lederer, Michael Gastegger, Kristof T. Sch\"utt, Michael Kampffmeyer, Klaus-Robert M\"uller, Oliver T. Unke
• Abstract summary: We introduce a method to automatically identify chemical moieties from atomic representations using message-passing neural networks. The versatility of our approach is demonstrated by enabling the selection of representative entries in chemical databases.
• Score: 11.50343898633327
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, the prediction of quantum mechanical observables with machine learning methods has become increasingly popular. Message-passing neural networks (MPNNs) solve this task by constructing atomic representations, from which the properties of interest are predicted. Here, we introduce a method to automatically identify chemical moieties (molecular building blocks) from such representations, enabling a variety of applications beyond property prediction, which otherwise rely on expert knowledge. The required representation can either be provided by a pretrained MPNN, or learned from scratch using only structural information. Beyond the data-driven design of molecular fingerprints, the versatility of our approach is demonstrated by enabling the selection of representative entries in chemical databases, the automatic construction of coarse-grained force fields, as well as the identification of reaction coordinates.

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