Issue 38, 2023

Automatic identification of chemical moieties

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 be 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.

Graphical abstract: Automatic identification of chemical moieties

Supplementary files

Article information

Article type
Paper
Submitted
11 Aug 2023
Accepted
18 Aug 2023
First published
30 Aug 2023
This article is Open Access
Creative Commons BY-NC license

Phys. Chem. Chem. Phys., 2023,25, 26370-26379

Automatic identification of chemical moieties

J. Lederer, M. Gastegger, K. T. Schütt, M. Kampffmeyer, K. Müller and O. T. Unke, Phys. Chem. Chem. Phys., 2023, 25, 26370 DOI: 10.1039/D3CP03845A

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