Automatic identification of chemical moieties

  • Jonas Lederer
  • Michael Gastegger
  • Kristof T. Schütt
  • Michael Christian Kampffmeyer
  • Klaus-Robert Müller

Publication details

  • Journal: Physical Chemistry, Chemical Physics - PCCP, vol. 25, p. 26370–26379–9, 2023
  • Publisher: Royal Society of Chemistry (RSC)
  • International Standard Numbers:
    • Printed: 1463-9076
    • Electronic: 1463-9084
  • Links:

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.