Chemometric and QSAR analysis of some thiadiazines as potential antifungal agents
Quantitative structure-activity relationship (QSAR) analysis has been performed in order to predict the antifungal activity of dihydroindeno and indeno thiadiazines against toxigenic fungus Aspergillus flavus. The studied compounds were classified according to their lipophilicity using the...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Faculty of Technology, Novi Sad
2017-01-01
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| Series: | Acta Periodica Technologica |
| Subjects: | |
| Online Access: | http://www.doiserbia.nb.rs/img/doi/1450-7188/2017/1450-71881748117K.pdf |
| _version_ | 1818149001748283392 |
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| author | Karadžić Milica Ž. Kovačević Strahinja Z. Jevrić Lidija R. Podunavac-Kuzmanović Sanja O. |
| author_facet | Karadžić Milica Ž. Kovačević Strahinja Z. Jevrić Lidija R. Podunavac-Kuzmanović Sanja O. |
| author_sort | Karadžić Milica Ž. |
| collection | DOAJ |
| description | Quantitative structure-activity relationship (QSAR) analysis has been
performed in order to predict the antifungal activity of dihydroindeno and
indeno thiadiazines against toxigenic fungus Aspergillus flavus. The studied
compounds were classified according to their lipophilicity using the
principal component analysis (PCA). The partial least square regression
(PLSR) was used to distinguish the most important molecular descriptors for
non-linear modeling. Artificial neural networks (ANNs) were applied for the
antifungal activity prediction. The best QSAR models were validated by
statistical parameters and graphical methods. High agreement between the
observed and predicted antifungal activity values indicated the good quality
of the derived QSAR models. The obtained QSAR-ANN models can be used to
predict the antifungal activity of dihydroindeno and indeno thiadiazines and
of structurally similar compounds. The modeling of the antifungal activity
can contribute to the synthesis of new antifungal agents with better ability
to protect food and feed from the mycotoxins. |
| first_indexed | 2024-12-11T13:00:06Z |
| format | Article |
| id | doaj.art-f5742be431b049afa6bcc01e83f99155 |
| institution | Directory Open Access Journal |
| issn | 1450-7188 2406-095X |
| language | English |
| last_indexed | 2024-12-11T13:00:06Z |
| publishDate | 2017-01-01 |
| publisher | Faculty of Technology, Novi Sad |
| record_format | Article |
| series | Acta Periodica Technologica |
| spelling | doaj.art-f5742be431b049afa6bcc01e83f991552022-12-22T01:06:30ZengFaculty of Technology, Novi SadActa Periodica Technologica1450-71882406-095X2017-01-0120174811712610.2298/APT1748117K1450-71881748117KChemometric and QSAR analysis of some thiadiazines as potential antifungal agentsKaradžić Milica Ž.0Kovačević Strahinja Z.1Jevrić Lidija R.2Podunavac-Kuzmanović Sanja O.3Faculty of Technology, Novi SadFaculty of Technology, Novi SadFaculty of Technology, Novi SadFaculty of Technology, Novi SadQuantitative structure-activity relationship (QSAR) analysis has been performed in order to predict the antifungal activity of dihydroindeno and indeno thiadiazines against toxigenic fungus Aspergillus flavus. The studied compounds were classified according to their lipophilicity using the principal component analysis (PCA). The partial least square regression (PLSR) was used to distinguish the most important molecular descriptors for non-linear modeling. Artificial neural networks (ANNs) were applied for the antifungal activity prediction. The best QSAR models were validated by statistical parameters and graphical methods. High agreement between the observed and predicted antifungal activity values indicated the good quality of the derived QSAR models. The obtained QSAR-ANN models can be used to predict the antifungal activity of dihydroindeno and indeno thiadiazines and of structurally similar compounds. The modeling of the antifungal activity can contribute to the synthesis of new antifungal agents with better ability to protect food and feed from the mycotoxins.http://www.doiserbia.nb.rs/img/doi/1450-7188/2017/1450-71881748117K.pdfartificial neural networksmycotoxinspartial least square regressionQSARthiadiazines |
| spellingShingle | Karadžić Milica Ž. Kovačević Strahinja Z. Jevrić Lidija R. Podunavac-Kuzmanović Sanja O. Chemometric and QSAR analysis of some thiadiazines as potential antifungal agents Acta Periodica Technologica artificial neural networks mycotoxins partial least square regression QSAR thiadiazines |
| title | Chemometric and QSAR analysis of some thiadiazines as potential antifungal agents |
| title_full | Chemometric and QSAR analysis of some thiadiazines as potential antifungal agents |
| title_fullStr | Chemometric and QSAR analysis of some thiadiazines as potential antifungal agents |
| title_full_unstemmed | Chemometric and QSAR analysis of some thiadiazines as potential antifungal agents |
| title_short | Chemometric and QSAR analysis of some thiadiazines as potential antifungal agents |
| title_sort | chemometric and qsar analysis of some thiadiazines as potential antifungal agents |
| topic | artificial neural networks mycotoxins partial least square regression QSAR thiadiazines |
| url | http://www.doiserbia.nb.rs/img/doi/1450-7188/2017/1450-71881748117K.pdf |
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