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Medicinal Chemistry
ISSN (Print): 1573-4064
ISSN (Online): 1875-6638
Epub Full Text Article
DOI: 10.2174/1573406410666140428144334      Price:  $95

Electronic-Topological and Neural Network Approaches to the Structure- Antimycobacterial Activity Relationships Study On Hydrazones Derivatives

Author(s): Fatma Kandemirli, Can Dogan Vurdu, Murat Alper Başaran, Hakan Sezgin Sayiner, Nathaly Shvets, Anatholy Dimoglo, Vasyl Kovalish and Turgay Polat
Electronic-Topological Method application and a variant of Feed Forward Neural Network (FFNN) identified as the Associative Neural Network to the compounds Hydrazones derivatives has been used to develop a prediction system of antituberculosis activity. The supervised learning has been performed using (ASNN) and categorized correctly 84.4%, or 38 compounds from 45. Ph1 pharmacophore consisting of 6 atoms and Ph2 pharmacophore consisting of 7 atoms were found. Anti-pharmacophore features being break of activity have also been revealed and APh1 found in 22 inactive molecules. Statistical analyses have been carried out by using the descriptors, such as EHOMO, ELUMO, ΔE, hardness, softness, chemical potential, electrophilicity index, exact polarizibility, total of electronic and zero point energies, dipole moment as independent variable in order to account for the dependent variable called inhibition efficiency. That observing several complexities, namely, linearity, nonlinearity and multi-co linearity at the same time leads data to be modeled using two different techniques called multiple regression and Artificial Neural Networks (ANNs) after computing correlations among descriptors in order to compute QSAR. Computations resulting in determination of some compounds with relatively high values of inhibition are presented
Antimycobacterial activity, Associative Neural Network, DFT, Electronic Topological Method, Hydrazidehydrazones, QSAR
Kastamonu University, Faculty of Engineering and Architecture, Biomedical Engineering Department, 37200, Kastamonu, Turkey