Premium
Graph Kernels for Molecular Similarity
Author(s) -
Rupp Matthias,
Schneider Gisbert
Publication year - 2010
Publication title -
molecular informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.481
H-Index - 68
eISSN - 1868-1751
pISSN - 1868-1743
DOI - 10.1002/minf.200900080
Subject(s) - cheminformatics , graph kernel , computer science , graph , similarity (geometry) , theoretical computer science , virtual screening , kernel (algebra) , property (philosophy) , support vector machine , kernel method , machine learning , artificial intelligence , mathematics , bioinformatics , polynomial kernel , combinatorics , drug discovery , biology , philosophy , epistemology , image (mathematics)
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.