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Automatic identification of chemical moieties.

Jonas Lederer1,2, Michael Gastegger1,2, Kristof T Schütt1,2

  • 1Berlin Institute of Technology (TU Berlin), 10587 Berlin, Germany. jonas.lederer@tu-berlin.de.

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Machine learning, specifically message-passing neural networks (MPNNs), can now automatically identify chemical moieties from atomic representations. This breakthrough enables diverse applications beyond property prediction, reducing reliance on expert knowledge.

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Area of Science:

  • Computational chemistry
  • Machine learning in chemistry

Background:

  • Machine learning methods, particularly message-passing neural networks (MPNNs), are increasingly used for predicting quantum mechanical observables.
  • MPNNs construct atomic representations to predict molecular properties.

Purpose of the Study:

  • To introduce a novel method for automatically identifying chemical moieties (molecular building blocks) from atomic representations.
  • To enable applications beyond property prediction that typically require expert chemical knowledge.

Main Methods:

  • Developing a method to automatically identify chemical moieties from atomic representations generated by MPNNs.
  • The atomic representations can be obtained from a pre-trained MPNN or learned from scratch using only structural information.

Main Results:

  • Demonstrated the ability to automatically identify chemical moieties, facilitating data-driven molecular design.
  • Showcased versatility through applications in selecting representative chemical database entries, constructing coarse-grained force fields, and identifying reaction coordinates.

Conclusions:

  • The developed method automates the identification of chemical moieties, expanding the utility of MPNN-generated representations.
  • This approach offers a versatile, data-driven alternative to expert knowledge for various chemical applications.