Evaluation of Virtual Screening Performance of Support Vector Machines Trained by Sparsely Distributed Active Compounds

2008 ◽  
Vol 48 (6) ◽  
pp. 1227-1237 ◽  
Author(s):  
X. H. Ma ◽  
R. Wang ◽  
S. Y. Yang ◽  
Z. R. Li ◽  
Y. Xue ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Virtual Screening ◽  
Support Vector ◽  
Active Compounds ◽  
Screening Performance ◽  
Vector Machines ◽  
Virtual Screening Performance

Extraction and Visualization of Potential Pharmacophore Points Using Support Vector Machines:  Application to Ligand-Based Virtual Screening for COX-2 Inhibitors

2005 ◽  
Vol 48 (22) ◽  
pp. 6997-7004 ◽  
Author(s):  
Lutz Franke ◽  
Evgeny Byvatov ◽  
Oliver Werz ◽  
Dieter Steinhilber ◽  
Petra Schneider ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Virtual Screening ◽  
Support Vector ◽  
Vector Machines ◽  
Cox 2 ◽  
Cox 2 Inhibitors

3D Ligand-Based Virtual Screening with Support Vector Machines

2011 ◽  
pp. 35-45
Author(s):  
Jean-Philippe Vert
Keyword(s):  
Support Vector Machines ◽  
Virtual Screening ◽  
3D Structure ◽  
Support Vector ◽  
Vector Machines ◽  
Discretization Step ◽  
Benchmark Datasets

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.

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