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

fYear

2008

fDate

12-15 Oct. 2008

Firstpage

1731

Lastpage

1736

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man and Cybernetics, 2008. SMC 2008. IEEE International Conference on

Conference_Location

Singapore

ISSN

1062-922X

Print_ISBN

978-1-4244-2383-5

Electronic_ISBN

1062-922X

Type

conf

DOI

10.1109/ICSMC.2008.4811538

Filename

4811538

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3114159