The Correlation of Biological Activity and Chemical Structure of Quinolizidinyl Derivatives as Inhibitor of Alzheimer’s Disease with Linear and Non-linear Models
In this research,QSAR study has been carried out on quinolizidinyl derivatives as potent inhibitors of Acetyl and Butyrylcholin esterase in Alzheimer’s disease. Despite significant research efforts in both industry and academia, there are currently no diseases modifying therapies available to treat...
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| Format: | Article |
| Language: | English |
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Iranian Chemical Society
2016-12-01
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| Series: | Analytical and Bioanalytical Chemistry Research |
| Subjects: | |
| Online Access: | http://www.analchemres.org/article_32617_8c09896540d613b276c26f1183078f8c.pdf |
| _version_ | 1818535317087453184 |
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| author | Ghasem Ghasemi Alireza Nemati Rashtehroodi |
| author_facet | Ghasem Ghasemi Alireza Nemati Rashtehroodi |
| author_sort | Ghasem Ghasemi |
| collection | DOAJ |
| description | In this research,QSAR study has been carried out on quinolizidinyl derivatives as potent inhibitors of Acetyl and Butyrylcholin esterase in Alzheimer’s disease. Despite significant research efforts in both industry and academia, there are currently no diseases modifying therapies available to treat this illness. Significant evidence suggests that the pathology of AD is linked to generation of β-amyloid peptides (Aβ) through proteolytic processing of amyloid precursor protein (APP). Genetic algorithm (GA), Jack-Knife and stepwise multiple linear regressions (stepwise-MLR) were used to create non-linear and linear QSAR models. The root-mean square errors of the training set and the validation set for GA–ANN model using Jack-Knife method, were 0.1406, 0.2165 and R2 was 0.90. Also, the R and R2 values in the gas phase were obtained as 0.88 and 0.78 from GA-stepwise MLR model, respectively. Also, we suggest that compounds No.4, 6, 10, 14, 24, 26 and 34 have the most appropriate structure for the design of drugs to pharmacists. Electronegativities, atomic polarizability and atomic van der Waals volumes were important descriptors in our study. Geometry optimization of compounds was carried out using the B3LYP method employing a 6–31G (d) basis set. |
| first_indexed | 2024-12-11T18:23:14Z |
| format | Article |
| id | doaj.art-3158f7dd75404da3a6ef81898bb29454 |
| institution | Directory Open Access Journal |
| issn | 2383-093X 2383-093X |
| language | English |
| last_indexed | 2024-12-11T18:23:14Z |
| publishDate | 2016-12-01 |
| publisher | Iranian Chemical Society |
| record_format | Article |
| series | Analytical and Bioanalytical Chemistry Research |
| spelling | doaj.art-3158f7dd75404da3a6ef81898bb294542022-12-22T00:55:10ZengIranian Chemical SocietyAnalytical and Bioanalytical Chemistry Research2383-093X2383-093X2016-12-013225326310.22036/abcr.2016.3261732617The Correlation of Biological Activity and Chemical Structure of Quinolizidinyl Derivatives as Inhibitor of Alzheimer’s Disease with Linear and Non-linear ModelsGhasem Ghasemi0Alireza Nemati Rashtehroodi1Department of Chemistry, Rasht Branch, Islamic Azad University, Rasht, IranDepartment of Chemistry, Payame Noor University, Sari Branch, Sari, IranIn this research,QSAR study has been carried out on quinolizidinyl derivatives as potent inhibitors of Acetyl and Butyrylcholin esterase in Alzheimer’s disease. Despite significant research efforts in both industry and academia, there are currently no diseases modifying therapies available to treat this illness. Significant evidence suggests that the pathology of AD is linked to generation of β-amyloid peptides (Aβ) through proteolytic processing of amyloid precursor protein (APP). Genetic algorithm (GA), Jack-Knife and stepwise multiple linear regressions (stepwise-MLR) were used to create non-linear and linear QSAR models. The root-mean square errors of the training set and the validation set for GA–ANN model using Jack-Knife method, were 0.1406, 0.2165 and R2 was 0.90. Also, the R and R2 values in the gas phase were obtained as 0.88 and 0.78 from GA-stepwise MLR model, respectively. Also, we suggest that compounds No.4, 6, 10, 14, 24, 26 and 34 have the most appropriate structure for the design of drugs to pharmacists. Electronegativities, atomic polarizability and atomic van der Waals volumes were important descriptors in our study. Geometry optimization of compounds was carried out using the B3LYP method employing a 6–31G (d) basis set.http://www.analchemres.org/article_32617_8c09896540d613b276c26f1183078f8c.pdfQSAR modelGenetic algorithmArtificial neural networkAlzheimer’s diseaseQuinolizidinyl derivatives |
| spellingShingle | Ghasem Ghasemi Alireza Nemati Rashtehroodi The Correlation of Biological Activity and Chemical Structure of Quinolizidinyl Derivatives as Inhibitor of Alzheimer’s Disease with Linear and Non-linear Models Analytical and Bioanalytical Chemistry Research QSAR model Genetic algorithm Artificial neural network Alzheimer’s disease Quinolizidinyl derivatives |
| title | The Correlation of Biological Activity and Chemical Structure of Quinolizidinyl Derivatives as Inhibitor of Alzheimer’s Disease with Linear and Non-linear Models |
| title_full | The Correlation of Biological Activity and Chemical Structure of Quinolizidinyl Derivatives as Inhibitor of Alzheimer’s Disease with Linear and Non-linear Models |
| title_fullStr | The Correlation of Biological Activity and Chemical Structure of Quinolizidinyl Derivatives as Inhibitor of Alzheimer’s Disease with Linear and Non-linear Models |
| title_full_unstemmed | The Correlation of Biological Activity and Chemical Structure of Quinolizidinyl Derivatives as Inhibitor of Alzheimer’s Disease with Linear and Non-linear Models |
| title_short | The Correlation of Biological Activity and Chemical Structure of Quinolizidinyl Derivatives as Inhibitor of Alzheimer’s Disease with Linear and Non-linear Models |
| title_sort | correlation of biological activity and chemical structure of quinolizidinyl derivatives as inhibitor of alzheimer s disease with linear and non linear models |
| topic | QSAR model Genetic algorithm Artificial neural network Alzheimer’s disease Quinolizidinyl derivatives |
| url | http://www.analchemres.org/article_32617_8c09896540d613b276c26f1183078f8c.pdf |
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