Chemometric and QSAR analysis of some thiadiazines as potential antifungal agents
| Autor: | Z Milica Karadzic, R Lidija Jevric, O Sanja Podunavac-Kuzmanovic, Z Strahinja Kovacevic |
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| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Antifungal Quantitative structure–activity relationship QSAR medicine.drug_class Chemistry General Engineering Combinatorial chemistry 03 medical and health sciences 030104 developmental biology mycotoxins partial least square regression lcsh:Technology (General) medicine lcsh:T1-995 thiadiazines artificial neural networks |
| Zdroj: | Acta Periodica Technologica, Vol 2017, Iss 48, Pp 117-126 (2017) |
| ISSN: | 2406-095X 1450-7188 |
| DOI: | 10.2298/apt1748117k |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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