mlsanityData to 7 Oct 2026

Graph neural networks for the prediction of molecular structure-property relationships

arXiv 2208.04852 ↗First posted 25 Jul 2022q-bio.BM cs.LG math.OCcited by 12

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Abstract (arXiv)

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.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 26 of its 54 references that are in the map. First posted on arXiv 25 Jul 2022.

Builds on

26 of 54 references.

References · 26

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14 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 6; 2025: 5; 2026: 1.

12 papers.

  1. 2024Thermodynamics-Consistent Graph Neural Networkscited by 12
  2. 2024GraphXForm: Graph transformer for computer-aided molecular designcited by 6
  3. 2025Molecular Machine Learning in Chemical Process Designcited by 5
  4. 2025Scaling Laws of Graph Neural Networks for Atomistic Materials Modelingcited by 4
  5. 2024Graph Neural Networks for Surfactant Multi-Property Predictioncited by 3
  6. 2024Predicting the Temperature-Dependent CMC of Surfactant Mixtures with Graph Neural Networkscited by 3
  7. 2025Evaluating machine learning models for predicting pesticide toxicity to honey beescited by 2
  8. 2025Federated Learning from Molecules to Processes: A Perspectivecited by 2
  9. 2024Predicting the Temperature Dependence of Surfactant CMCs Using Graph Neural Networkscited by 1
  10. 2024Graph Residual based Method for Molecular Property Predictioncited by 0
  11. 2025DeepEOSNet: Capturing the dependency on thermodynamic state in property prediction taskscited by 0
  12. 2026Periodic Topological Deep Learning for Polymer Design and Discoverycited by 0