The author reviews an approach, proposed recently by Mahé, Ralaivola, Stoven, and Vert (2006), for ligand-based virtual screening with support vector machines using a kernel based on the 3D structure of the molecules. The kernel detects putative 3-point pharmacophores, and generalizes previous approaches based on 3-point pharmacophore fingerprints. It overcomes the categorization issue associated with the discretization step usually required for the construction of fingerprints, and leads to promising results on several benchmark datasets.
APPLICATIONS OF SUPPORT VECTOR MACHINES AS A ROBUST TOOL IN HIGH THROUGHPUT VIRTUAL SCREENING