Title :
Support vector machine application in drug discovery of aldose reductase inhibitors
Author :
Patra, J.C. ; Li, L. ; Meher, P.K.
Author_Institution :
Sch. of Comput. Eng., Nanyang Technol. Univ., Singapore
Abstract :
Using support vector machine (SVM) function approximation, in this paper, we present the quantitative structure-activity relationship (QSAR) among the known aldose reductase inhibitors (ARIs). The two physical descriptors of a molecule, namely the electronegativity and the molar volume are evaluated by SVM. SVM is found to work better than multi-layer perceptron (MLP).
Keywords :
medical computing; multilayer perceptrons; support vector machines; SVM function approximation; aldose reductase inhibitors; drug discovery; multilayer perceptron; quantitative structure-activity relationship; support vector machine application; Artificial neural networks; Biochemistry; Diabetes; Drugs; Function approximation; Inhibitors; Machine learning; Multilayer perceptrons; Sugar; Support vector machines; ARIs; QSAR; SVM function approximation;
Conference_Titel :
Systems, Man and Cybernetics, 2008. SMC 2008. IEEE International Conference on
Conference_Location :
Singapore
Print_ISBN :
978-1-4244-2383-5
Electronic_ISBN :
1062-922X
DOI :
10.1109/ICSMC.2008.4811538
