Two New Graph Kernels and Applications to Chemoinformatics

Two new graphs kernels in chemoinformatics

Chemoinformatics is a well established research field concerned with the discovery of molecule’s properties through informational techniques. Computer science’s research fields mainly concerned by chemoinformatics are machine learning and graph theory. From this point of view, graph kernels provide a nice framework combining machine learning graph theory techniques. Such kernels prove their eff...

Graph kernels for chemoinformatics – a critical discussion

We analyze the use, advantages, and drawbacks of graph kernels in chemoin-formatics, including a comparison of kernel-based approaches with other methodology, as well as examples of applications. Kernel-based machine learning [1], now widely applied in chemoinformatics, delivers state-of-the-art performance [2] in tasks like classification and regression. Molecular graph kernels [3] are a recen...

Graph kernels in chemoinformatics

Graph Kernels: Crossing Information from Different Patterns Using Graph Edit Distance

Graph kernels allow to define metrics on graph space and constitute thus an efficient tool to combine advantages of structural and statistical pattern recognition fields. Within the chemoinformatics framework, kernels are usually defined by comparing number of occurences of patterns extracted from two different graphs. Such a graph kernel construction scheme neglects the fact that similar but n...

Relevant Cycle Hypergraph Representation for Molecules

Chemoinformatics aims to predict molecule’s properties through informational methods. Some methods base their prediction model on the comparison of molecular graphs. Considering such a molecular representation, graph kernels provide a nice framework which allows to combine machine learning techniques with graph theory. Despite the fact that molecular graph encodes all structural information of ...