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.
@article{arxiv.2203.16205,
title = {Automatic Identification of Chemical Moieties},
author = {Jonas Lederer and Michael Gastegger and Kristof T. Schütt and Michael Kampffmeyer and Klaus-Robert Müller and Oliver T. Unke},
journal= {arXiv preprint arXiv:2203.16205},
year = {2023}
}