https://qsardb.org:443/repository The QsarDB repository stores, indexes, preserves, and distributes QSAR/QSPR models. Wed, 25 Mar 2026 20:19:12 GMT 2026-03-25T20:19:12Z http://hdl.handle.net/10967/270 Histone deacetylases (HDACs) and Rho-associated coiled-coil containing protein kinases (ROCKs) are critical regulators of tumor development, progression and immunomodulation, particularly in aggressive cancers such as pancreatic ductal adenocarcinoma (PDAC) and triple negative breast cancer (TNBC). Previous in silico and in vitro studies have demonstrated the therapeutic potential of multitarget HDAC/ROCK inhibition, leading to the identification of C-9 as the first-in-class multitarget HDAC/ROCK inhibitor. In the present study, C-9 served as a lead compound for the rational design of a new series of multitarget inhibitors. Structure-based drug design (SBDD) was used to develop a series of novel HDAC/ROCK multitarget inhibitors. Molecular docking and MM-GBSA binding free energy analysis confirmed strong and specific interactions with the catalytic sites of both targets. Based on these computational insights, ten compounds were synthesized and biologically evaluated using enzyme inhibition and cancer cell assays. Among the synthesized compounds, C-35 and C-40 showed remarkable cytotoxic activity against TNBC cell lines, with IC50 values of 17 µM and 27 µM against MDA-MB-231 cells and about 35 µM against MDA-MB-468 cells, respectively, outperforming known selective HDAC6 and ROCK inhibitors such as tubastatin A and fasudil. Further mechanistic studies revealed that these compounds induce early apoptosis, arrest cell cycle progression and downregulate PD-L1 expression. Remarkably, C-35 also increased the expression of MICA in MDA-MB-468 cells, possibly promoting the recognition and elimination of tumor cells by immune cells. To further support rational drug optimization, we used synthesized compounds and their IC50 values for the enzymes to form an external blind validation set that enabled the development of machine learning-based quantitative structure-activity relationships (QSAR) models for HDAC6 and ROCK2. These models, developed for the first time for both targets, showed strong predictive performance and were integrated into a comprehensive CADD workflow combining structure-based (molecular docking and MM-GBSA) and ligand-based (QSAR) methods for drug development. Overall, this study highlights the anti-cancer, anti-invasive, anti-migratory and immunomodulatory potential of multitarget HDAC/ROCK inhibition for the treatment of TNBC. Finally, it creates a robust computational-experimental pipeline for future development and optimization of multitarget HDAC/ROCK inhibitors. Tue, 13 May 2025 15:03:38 GMT http://hdl.handle.net/10967/270 2025-05-13T15:03:38Z http://hdl.handle.net/10967/269 Some adverse effects of hydroxylated polychlorinated biphenyls (OH-PCBs) in humans are presumed to be initiated via thyroid hormone receptor (TR) binding. Due to the trial-and-error approach adopted for OH-PCB selection in previous studies, experiments designed to test the TR binding hypothesis mostly utilized inactive OH-PCBs, leading to considerable waste of time, effort and other material resources. In this paper, linear discriminant analysis (LDA) and binary logistic regression (LR) were used to develop classification models to group OH-PCBs into active and inactive TR agonists using radial distribution function (RDF) descriptors as predictor variables. The classifications made by both LDA and LR models on the training set compounds resulted in an accuracy of 84.3%, sensitivity of 72.2% and specificity of 90.9%. The areas under the ROC curves, constructed with the training set data, were found to be 0.872 and 0.880 for LDA and LR models, respectively. External validation of the models revealed that 76.5% of the test set compounds were correctly classified by both LDA and LR models. These findings suggest that the two models reported in this paper are good and reliable for classifying OH-PCB congeners into active and inactive TR agonists. Wed, 07 May 2025 13:27:28 GMT http://hdl.handle.net/10967/269 2025-05-07T13:27:28Z http://hdl.handle.net/10967/266 Ionic liquids are known as green solvents, which makes accurate prediction of gas–ionic liquid partition coefficients (log K) important from the perspective of various industrial applications. A gas–ionic liquid is a multicomponent system, but is usually modelled by the structural properties of one component, the solute. The integration of structural descriptors of all three components, solute, cation, and anion, into a single computational model has not been achieved. To do this, a machine learning approach was applied to a large collected dataset consisting of 6,531 experimental log K values, including data series for 170 solutes and 138 ionic liquids. The Multiple Linear Regression (MLR) and Random Forest Regression (RF) approaches were compared, both of which applied stepwise forward descriptor selection. The best MLR model achieved a cross-validated coefficient of determination (Rcv2) of 0.795 and an external validation coefficient of determination (R2) of 0.801, while the RF model demonstrated significant increase in performance with cross-validated Rcv2 of 0.965 and external validation R2 of 0.957. The descriptors included in the models showed that the description and prediction of log K is significantly improved when structural properties of all three components of the system (solute, cation, and anion) are taken into account. When comparing the linear and non-linear RF models, the presence of molecular descriptors of different components was significantly increased in the latter. The molecular descriptors in the models highlighted the roles of dispersion forces, dipolar interactions, and hydrogen bonding in solute–ionic liquid partitioning. The study provides thoroughly-analyzed predictive models for estimating gas–ionic liquid partition coefficients and provides structure-level insights into solute–ionic-liquid interactions, facilitating the rational design of ionic liquids and expanding the range of solutes for various applications. Mon, 17 Mar 2025 11:45:47 GMT http://hdl.handle.net/10967/266 2025-03-17T11:45:47Z http://hdl.handle.net/10967/265 Drug substances in water bodies and groundwater have become a significant threat to the surrounding environment. This study focuses on the ability of the nanoporous carbon materials to remove ciprofloxacin from aqueous solutions under specific experimental conditions and on the development of the mathematical model that would allow describing the molecular interactions of the adsorption process and calculating the adsorption capacity of the material. Thus, based on the adsorption measurements of the 87 carbon materials, it was found that, depending on the porosity and pore size distribution, adsorption capacity values varied between 55 and 495 mg g-1. For a more detailed analysis of the effects of different carbon textures and pores characteristics, a Quantitative nano-Structure-Property Relationship (QnSPR) was developed to describe and predict the ability of a nanoporous carbon material to remove ciprofloxacin from aqueous solutions. The adsorption capacity of potential nanoporous carbon-based adsorbents for the removal of ciprofloxacin was shown to be sufficiently accurately described by a three-parameter multi-linear QnSPR equation (R² = 0.70). This description was achieved only with parameters describing the texture of the carbon material such as specific surface area (Sdft) and pore size fractions of 1.1-1.2 nm (𝑉𝑁2[1.1−1.2]) and 3.3-3.4 nm (𝑉𝑁2[3.3−3.4]) for pores Fri, 25 Oct 2024 07:41:49 GMT http://hdl.handle.net/10967/265 2024-10-25T07:41:49Z