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Journal of Biomedicine and Biotechnology
Volume 2010, Article ID 693031, 12 pages
http://dx.doi.org/10.1155/2010/693031
Research Article

ETM-ANN Approach Application for Thiobenzamide and Quinolizidine Derivatives

1Faculty of Education, Erciyes University, 38039 Kayseri, Turkey
2Department of Chemistry, Kocaeli University, 41000 Izmit, Turkey
3Niğde University, Department of Chemistry, 41000 Niğde, Turkey
4Biomedical Department, Institute of Bioorganic Chemistry and Petrochemistry, Kyiv 02660, Ukraine
5Departamento de Quimica, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal
6Department of Chemistry, Eskişehir Osmangazi University, 26480 Eskişehir, Turkey
7Department of Chemistry, North West University (Mafikeng Campus), Private Bag X2046, Mmabatho 2735, South Africa

Received 9 March 2010; Revised 15 May 2010; Accepted 30 June 2010

Academic Editor: Ying Xu

Copyright © 2010 M. Saracoglu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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, , , , 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 .