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Research Article Open access CC BY 4.0

Integrated In Silico and Chromatographic Evaluation of the Biological Properties of Novel Bis-Substituted Thiocarbohydrazone Derivatives

Suzana Apostolov, Dragana Mekić, Gorana Mrđan, Gyöngyi Vastag

Organics · pp. 19–19 · Published 12 May 2026

10.3390/org7020019

Abstract

Thiocarbohydrazone derivatives represent a highly significant class in medicinal chemistry, characterized by a versatile scaffold defined with a thiocarbonyl (C=S) core and one or two imine (–C=N–) functionalities, allowing for precise modulation of their physicochemical and biological properties. The biological potential of a series of novel bis-substituted thiocarbohydrazone derivatives was predicted and evaluated through comprehensive in silico analysis. All investigated compounds complied with Lipinski’s Rule of 5, with most also satisfying the Rule of 3 while simultaneously exhibiting favorable pharmacokinetic properties and low predicted ecotoxicity. To substantiate these findings and elucidate the influence of para-substituents, chromatographic behavior of the studied derivatives was evaluated using reversed-phase thin-layer chromatography (RP-TLC). Initial linear regression analysis revealed statistically significant correlations between chromatographic parameters and in silico-derived descriptors of lipophilicity, pharmacokinetics, and ecotoxicity. Furthermore, cluster analysis and principal component analysis provided a robust and unambiguous interpretation of the structure–property relationships, highlighting substituent polarity as the leading factor controlling the bioactivity of bis-substituted thiocarbohydrazones, although the contribution of electronic effects cannot be neglected. Moreover, RM0 correlates with lipophilicity and pharmacokinetics, whereas m reflects ecotoxicity. Collectively, these findings emphasize the critical role of subtle structural variations in shaping the overall properties of these novel derivatives.

Lipophilicity Chemistry In silico Principal component analysis Quantitative structure–activity relationship Linear regression Biological system Biological activity

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