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@ARTICLE{Rittig:917553,
      author       = {Rittig, Jan G. and Gao, Qinghe and Dahmen, Manuel and
                      Mitsos, Alexander and Schweidtmann, Artur M.},
      title        = {{G}raph neural networks for the prediction of molecular
                      structure-property relationships},
      publisher    = {arXiv},
      reportid     = {FZJ-2023-00755},
      year         = {2022},
      abstract     = {Molecular property prediction is of crucial importance in
                      many disciplines such as drug discovery, molecular biology,
                      or material and process design. The frequently employed
                      quantitative structure-property/activity relationships
                      (QSPRs/QSARs) characterize molecules by descriptors which
                      are then mapped to the properties of interest via a linear
                      or nonlinear model. In contrast, graph neural networks, a
                      novel machine learning method, directly work on the
                      molecular graph, i.e., a graph representation where atoms
                      correspond to nodes and bonds correspond to edges. GNNs
                      allow to learn properties in an end-to-end fashion, thereby
                      avoiding the need for informative descriptors as in
                      QSPRs/QSARs. GNNs have been shown to achieve
                      state-of-the-art prediction performance on various property
                      predictions tasks and represent an active field of research.
                      We describe the fundamentals of GNNs and demonstrate the
                      application of GNNs via two examples for molecular property
                      prediction.},
      keywords     = {Biomolecules (q-bio.BM) (Other) / Machine Learning (cs.LG)
                      (Other) / Optimization and Control (math.OC) (Other) / FOS:
                      Biological sciences (Other) / FOS: Computer and information
                      sciences (Other) / FOS: Mathematics (Other)},
      cin          = {IEK-10},
      cid          = {I:(DE-Juel1)IEK-10-20170217},
      pnm          = {1121 - Digitalization and Systems Technology for
                      Flexibility Solutions (POF4-112)},
      pid          = {G:(DE-HGF)POF4-1121},
      typ          = {PUB:(DE-HGF)25},
      doi          = {10.48550/ARXIV.2208.04852},
      url          = {https://juser.fz-juelich.de/record/917553},
}