We present an approach for generating vectorial represen-
tations of graphs for machine learning applications based on a sensoric
response principle and multiple graph kernels. The sensor perspective re-
duces the graph kernel computations significantly. Thus, multiple kernel
(relevance) learning can be realized using the interpretable generalized ma-
trix learning vector quantization (GMLVQ) classifier. Results obtained in
small molecule classification serve as proof of concept.