QSAR-ANALYSIS OF THE 4-THIAZOLIDINONE-RELATED LIBRARIES FOR THE PREDICITING OF ANTITRIPANOSOMAL PROPERTIES OF NOVEL DERIVATIVES
DOI:
https://doi.org/10.11603/mcch.2410-681X.2020.i4.11738Keywords:
thiazolidones, antitrypanosomal activityAbstract
Introduction. Derivatives of thiazolidinone and related heterocycles are a source of new antiparasitic agents, including molecules with antitrypanosomal properties. A number of studies of the quantitative structure – antitrypanosomal activity relationship based on different approaches of computer chemistry have been found in the relevant scientific sources. Although most studies belong to the so-called – multitarget, when the studied set include the results of other types of antiparasitic activities. Development of new QSAR-models of thiazolidinone derivatives with antitrypanosomal properties will allow to outline the directions of directed design of new antiparasitic agents based on thiazole and thiazolidinone cycles.
The aim of the study – to establish quantitative relationships between structure-antitrypanosomal activity within libraries of thizolidinones and related heterocycles.
Research Methods. The development of mathematical models based on QSAR-analysis was performed using the online platform Online Chemical Database.
Results and Discussion. Analysis of the quantitative structure-activity relationship was performed using a mathematical model of associative neural networks (ASNN: Associative Neural Networks) and the Random Forest regression method (RFR: Random Forest regression) based on set of compounds including isothiocoumarin-3-carboxylic acid derivatives, thiopyranothiazoles and 4-thiazolidinone-imidazothiadiazoles with the established trypanocidal activity against Trypanosoma brucei brucei and Trypanosoma brucei gambiense. The best predictive capacity for the group of isothiocoumarin-3-carboxylic acids and thiopyrano[2,3-d][1,3]thiazol-2-ones was calculated using the Random Forest algorithm. The model calculated on the basis of the Random Forest algorithm for the group of imidazothiadiazoles has the highest predictive power with a value of R2 = 0.96.
Conclusions. Based on the methods of associative neural networks and Random Forest regression, a series of prognostic models have been developed for the predicting of antiparasitic activity of different 4-thiazolidinone derivatives and further development of the optimization directions for novel biologically active molecules with trypanocidal properties.
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