PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Virtual screening with support vector machines and structure kernels
Pierre Mahé and Jean-Philippe Vert
Combinatorial Chemistry & High Throughput Screening Volume 12, Number 4, pp. 409-423, 2009.


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.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:4436
Deposited By:Jean-Philippe Vert
Deposited On:13 March 2009