Beyond descriptor vectors: QSAR modelling using structural similarity

Kernel based machine learning methods like support vector machines or gaussian processes have gained increasing attention for QSAR modelling in recent years. One of the most interesting aspects of this method is the analogy between the kernel and a similarity measure. Each similarity measure that fulfils the kernel properties can be used as a kernel. But despite the possibility to incorporate structural/ topological information directly into the similarity score, as it is done by state-of-the-art methods like feature tree s[1], most studies that use kernel methods are limited to classical descriptor representations [2].