# Literature Review: Graph Kernels in Chemoinformatics

arXiv: https://arxiv.org/abs/2208.04929 · HTML: https://mlsanity.com/paper/2208.04929/

## Key facts

- Cited by 1 papers 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 (most cited first, 6 of 6)

- [A Survey on Graph Kernels](https://mlsanity.com/paper/1903.11835/) (2019)
- [Wasserstein Weisfeiler-Lehman Graph Kernels](https://mlsanity.com/paper/1906.01277/) (2019)
- [Graph Kernels: State-of-the-Art and Future Challenges](https://mlsanity.com/paper/2011.03854/) (2020)
- [On Valid Optimal Assignment Kernels and Applications to Graph Classification](https://mlsanity.com/paper/1606.01141/) (2016)
- [The Power of the Weisfeiler-Leman Algorithm for Machine Learning with Graphs](https://mlsanity.com/paper/2105.05911/) (2021)
- [A Unifying View of Explicit and Implicit Feature Maps of Graph Kernels](https://mlsanity.com/paper/1703.00676/) (2017)

## Built on by (most cited first, 1 of 1)

- [Using Weisfeiler-Leman Features for Algorithm Selection in Constraint Optimisation](https://mlsanity.com/paper/2610.12119/) (2026)

## 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.