Automatic Identification of Chemical Moieties

by   Jonas Lederer, et al.

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.


page 3

page 13


Flexible dual-branched message passing neural network for quantum mechanical property prediction with molecular conformation

A molecule is a complex of heterogeneous components, and the spatial arr...

A data-driven interpretation of the stability of molecular crystals

Due to the subtle balance of intermolecular interactions that govern str...

Equivariant message passing for the prediction of tensorial properties and molecular spectra

Message passing neural networks have become a method of choice for learn...

Rxn Hypergraph: a Hypergraph Attention Model for Chemical Reaction Representation

It is fundamental for science and technology to be able to predict chemi...

Machine learning of solvent effects on molecular spectra and reactions

Fast and accurate simulation of complex chemical systems in environments...

Multi-scale approach for the prediction of atomic scale properties

Electronic nearsightedness is one of the fundamental principles governin...

Non-Uniform Gaussian Blur of Hexagonal Bins in Cartesian Coordinates

In a recent application of the Bokeh Python library for visualizing phys...