Virtual Screening with Support Vector Machines and Structure Kernels

Title: Virtual Screening with Support Vector Machines and Structure Kernels

Volume: 12 Issue: 4

Author(s): Pierre Mahe and Jean-Philippe Vert

Affiliation:

Keywords: Virtual screening, graph kernels, support vector machines

Abstract: 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.

Cite this article as:

Mahe Pierre and Vert Jean-Philippe, Virtual Screening with Support Vector Machines and Structure Kernels, Combinatorial Chemistry & High Throughput Screening 2009; 12 (4) . https://dx.doi.org/10.2174/138620709788167926