Assignment kernels for chemical compounds

During the last years kernel methods like the support vector machine (SVM) have gained a growing interest in machine learning. One of the strengths of this approach is the ability to deal easily with arbitrarily structured data by means of the kernel function. In this paper we propose a kernel for chemical compounds which is based on the idea of computing optimal assignments between atoms of two different molecules including information about their neighborhood. As a byproduct this leads to a new class of kernel functions. We demonstrate how the necessary computations can be carried out efficiently. We compare our method against the marginalized graph kernels by Kashima et al. and show its good performance on classifying toxicological and human intestinal absorption data.