  Support vector machines and kernel methods have recently gained considerable
attention in chemoinformatics. They offer generally good performance for
problems of supervised classification or regression, and provide a flexible and
computationally efficient framework to include relevant information and prior
knowledge about the data and problems to be handled. In particular, with kernel
methods molecules do not need to be represented and stored explicitly as
vectors or fingerprints, but only to be compared to each other through a
comparison function technically called a kernel. While classical kernels can be
used to compare vector or fingerprint representations of molecules, completely
new kernels were developed in the recent years to directly compare the 2D or 3D
structures of molecules, without the need for an explicit vectorization step
through the extraction of molecular descriptors. While still in their infancy,
these approaches have already demonstrated their relevance on several toxicity
prediction and structure-activity relationship problems.
