Literature Review: Graph Kernels in Chemoinformatics
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Abstract (arXiv)
The purpose of this review is to introduce the reader to graph kernels and the corresponding literature, with an emphasis on those with direct application to chemoinformatics. Graph kernels are functions that allow for the inference of properties of molecules and compounds, which can help with tasks such as finding suitable compounds in drug design. The use of kernel methods is but one particular way two quantify similarity between graphs. We restrict our discussion to this one method, although popular alternatives have emerged in recent years, most notably graph neural networks.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 6 of its 67 references that are in the map. First posted on arXiv 9 Aug 2022.
Builds on
6 of 67 references.
References · 6
- 2019A Survey on Graph Kernelscited by 148
- 2019Wasserstein Weisfeiler-Lehman Graph Kernelscited by 104
- 2020Graph Kernels: State-of-the-Art and Future Challengescited by 63
- 2016On Valid Optimal Assignment Kernels and Applications to Graph Classificationcited by 49
- 2021The Power of the Weisfeiler-Leman Algorithm for Machine Learning with Graphscited by 19
- 2017A Unifying View of Explicit and Implicit Feature Maps of Graph Kernelscited by 4
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Citing papers per year: 2026: 1.
1 paper.