Rupp M Schneider G 2010 Molecular similarity measures are important for many cheminformatics applications like ligand-based virtual screening and quantitative structure-property relationships. Graph kernels are formal similarity measures defined directly on graphs, such as the (annotated) molecular structure graph. Graph kernels are positive semi-definite functions, i.e., they correspond to inner products. This property makes them suitable for use with kernel-based machine learning algorithms such as support vector machines and Gaussian processes. We review the major types of kernels between graphs (based on random walks, subgraphs, and optimal assignments, respectively), and discuss their advantages, limitations, and successful applications in cheminformatics. https://sonar.ch/global/documents/265817 eng info:eu-repo/semantics/altIdentifier/doi/10.1002/minf.200900080 info:eu-repo/semantics/altIdentifier/pmid/27463053 Molecular informatics. - 2010 Graph kernels Machine learning Molecular similarity Structure graph Graph Kernels for Molecular Similarity. http://purl.org/coar/resource_type/c_6501