ETM-ANN Approach Application for Thiobenzamide and Quinolizidine Derivatives. (7th September 2010)

• Record Type: Journal Article
• Authors: Saracoglu, M.; Kandemirli, F.; Kovalishyn, V.; Arslan, T.; Ebenso, E. E.
• Other Names: Xu Ying Academic Editor.
• Is Part Of: Journal of biomedicine and biotechnology. Volume 2010(2010)
• Publication Date: 2010-09-07
• Subjects: Medicine -- Periodicals; Biology -- Periodicals; Biotechnology -- Periodicals; Medicine; Biology; Biotechnology; Médecine; Biologie; Biotechnologie; Biology; Biotechnology; Medicine; Biotechnology; Biomedicine; Electronic journals; Periodical; Periodicals; Electronic journals; 610
• DOI: 10.1155/2010/693031
• Languages: English
• ISSNs: 1110-7243

Abstract

Abstract : The structure anti-influenza activity relationships of thiobenzamide and quinolizidine derivatives, being influenza fusion inhibitors, have been investigated using the electronic-topological method (ETM) and artificial neural network (ANN) method. Molecular fragments specific for active compounds and breaks of activity were calculated for influenza fusion inhibitors by applying the ETM. QSAR descriptors such as molecular weight, E HOMO, E LUMO, Δ E, chemical potential, softness, electrophilicity index, dipole moment, and so forth were calculated, and it was found to give good statistical qualities (classified correctly 92%, or 48 compounds from 52 in training set, and 69% or 9 compounds from 13 in the external test set). By using multiple linear regression, several QSAR models were performed with the help of calculated descriptors and the compounds activity data. Among the obtained QSAR models, statistically the most significant one is the one of skeleton 1 with R 2 = 0.999 .