Emin Sarıpınar

Several new Schiff‐based compounds were synthesized, and their chemical structures were confirmed usingFT‐IR, ¹ H‐NMR, ¹³ C‐NMR, and HRMS. The compounds were tested in the prostate cancer cell line (PC3) for 72 h. The results demonstrated that compounds NZ10 , NZ9 , NZ7 , and NZ4 had high cytotoxic effects on the PC3 cell line with IC ₅₀ values of 10.54, 14.58, 15.85, and 16.48 µM, respectively. The modulator roles of selected compounds on apoptotic signaling were tested by their effect on mRNA expression levels of BAX , BID , BCL2 , and BCL‐xL using RT‐qPCR. Furthermore, the effects on oxidative stress levels were examined by measuring total oxidant and antioxidant levels in cells. The results revealed that selected compounds significantly upregulated pro‐apoptotic BAX and BID , whereas they downregulated anti‐apoptotic BCL2 and BCL‐xL levels. Besides, compounds were found to significantly increase the oxidative status in cells. The binding potential of relatively active compounds to BCL‐2 was assessed through molecular docking and molecular dynamics (MD) simulations. The docking study demonstrated that compounds had binding potential to the structure but were weaker than the standard drug, venetoclax. MD simulation analysis showed that complexes of the compounds with higher affinity formed stable complexes with the structure.

Fifteen new guanidine‐based imidazole ring‐containing compounds were synthesized and their structures were characterized by ¹ H NMR, ¹³ C NMR, mass spectroscopy, and x‐rays methods. The anticancer activity of the new derivatives was tested against hypopharyngeal cancer cells. Among all the compounds, compound 2h showed the most prominent and promising anticancer potential with a relatively lower IC ₅₀ value against FaDu cells. Quantum chemical characterizations of 15 new guanidine‐based imidazole ring‐containing compounds were performed with the Gaussian software for the B3LYP, HF, and M062X levels in the 6–31++g(d,p) basis sets. The hypopharyngeal FaDu carcinoma cell proteins (PDB ID: 3NUP, 3RCD, 3V99, 5EW3, 5HQY) were among the proteins that were tested for the activities of these 15 compounds. ADME/T analysis calculations were performed on these 15 compounds in order to predict their effects and reactions in human metabolism.

A three-component Mannich reaction catalyzed by a ZrO(L-proline)₂ hybrid catalyst was investigated for the synthesis of β-amino ketones under mild conditions. Aromatic aldehydes, anilines, and cyclohexanone were reacted in the presence of the catalyst to afford the corresponding products in moderate yields (32-68%) with anti:syn ratios ranging from 83:17 to 100:0. Catalyst screening demonstrated that ZrO(L-proline)₂ promoted improved diastereoselectivity compared with related zinc and zirconium salts. Solvent effects strongly influenced both yield and stereochemical outcome, with p-xylene affording complete anti-selectivity at lower conversion. Preliminary substrate scope studies confirmed the applicability of the catalytic system toward substituted aromatic aldehydes. The results suggest that cooperative Lewis acid/organic ligand activation may contribute to stereochemical control in the Mannich transformation.

Background/aim: Pyrazole derivatives are of growing interest due to their diverse pharmacological activities. However, their biological activity is often highly sensitive to subtle structural modifications. Existing quantitative structure–activity relationships (QSAR) approaches frequently fail to capture the conformational flexibility and nonlinear structure–activity relationships (SAR) of such heterocyclic scaffolds, creating a gap in the accurate prediction of their biological profiles. Therefore, there is a strong need for more robust and predictive computational frameworks. This study addresses this gap by integrating four-dimensional (4D)-QSAR descriptors with hybrid machine learning (ML) techniques to improve predictive accuracy and provide a more reliable tool for structure-based drug design. In this work, it was aimed to investigate the SAR of a series of pyrazole-based compounds using this advanced integrative computational strategy. Materials and methods: The dataset consisted of 54 pyrazole derivatives, of which 50 compounds were used for model construction and 4 compounds were reserved as a test set for validation. Although the test set was limited in size, the selected compounds were structurally representative of the training set, sharing the same core scaffold while covering different substitution patterns and biological activity values. The 4D-QSAR approach included multiple conformations of each compound and utilized matrix-based representations of geometric and electronic properties to capture dynamic molecular behavior. A pharmacophore model was generated using EMRE software based on the spatial and electronic features of used compounds. EMRE is an in-house software developed by our research group. It has been employed in several previously published 4D-QSAR studies for electron-conformational matrix of contiguity construction, pharmacophore modeling, descriptor matrix generation, and activity prediction. EMRE operates on standard geometric and electronic descriptors derived from quantum-chemical calculations, ensuring methodological transparency and reproducibility despite its proprietary implementation. Comparable performance trends obtained with EMRE-based 4D-QSAR models have been reported in previous studies, supporting the validity of the software for pharmacophore-driven QSAR analysis (Şahin et al., 2011; Sahin and Saripinar, 2020; Sahin et al., 2021). Using this framework, a total of 204 molecular descriptors were computed using Spartan 07. To reduce redundancy and prevent overfitting, descriptor selection was optimized through a genetic algorithm (GA)-based procedure (Fernandez et al., 2011), and only statistically significant descriptors with low intercorrelation were retained for model construction. Subsequently, multiple ML algorithms, including artificial neural network, decision tree, and hybrid models, were evaluated to enhance prediction accuracy. Results: Among all the tested models, the gradient boosting machine and random forest (GBM+RF) hybrid algorithm yielded the highest predictive performance, with an R² value of 0.99978. To assess the robustness of the ML models, the training and validation procedures were repeated using different random seed initializations. The resulting performance metrics showed only minor variations across runs, indicating that the predictive performance of the GBM, RF, and GBM+RF hybrid models was not sensitive to random seed selection. The overall dataset comprised 54 pyrazole derivatives, with 50 molecules used for model construction and 4 reserved for validation. Although the high R² value indicates strong internal consistency, it should be interpreted with caution due to the relatively small sample sizes for the construction of the model and the test subset. Overall, the integration of 4D-QSAR and ML approaches demonstrated strong predictive capability and effectively captured the key geometric and electronic features associated with biological activity. Conclusion: The electron conformational-GA computational strategy provides a robust framework for the rational design and virtual screening of pyrazole derivatives, leveraging multi-conformer modeling and a diverse set of molecular descriptors to identify potentially active compounds. The study was limited by the small size of the training and test set and the absence of experimental validation, which may constrain the generalizability of the findings. Future work could address these limitations by applying the model to larger and more diverse ligand dataset, performing virtual screening of compound libraries to discover novel hits, and validating promising candidates through in vitro assays. Overall, these findings support the potential of this scaffold for drug discovery, while further experimental studies are warranted to confirm the predicted activities and refine the predictive power of the model.

The discovery of therapeutic agents is important for cancer treatment. Although dozens of agents have been used in cancer treatments, cancer continues to be a serious disease with a high mortality rate. There is an urgent need for the discovery of new anticancer agents, especially for the long‐term treatment of prostate cancer. Thanks to the expansion of cancer‐related data, we now can synthesize new therapeutic agents using biological methods. Carboxamide derivatives ( 5a–r compounds) were synthesized as a potential anticancer agent. Cell proliferation assays showed that they had antiproliferative activity against the human prostate cancer cell line PC3, particularly at the 25 μM dose. In summary, our findings revealed that 5g–l and 5m–r groups are effective agents against the prostate cancer cell line. In conclusion, Gaussian calculations were carried out in order to investigate carboxamide derivatives ( 5a–r compounds) at the B3LYP, HF, and M062X levels, using the 6–31++g(d,p) basis set. Molecular docking calculations were carried out on a variety of proteins, including the protein that is associated with prostate cancer (PDB ID: 3RUK and 3A99). Calculations using the ADME/T method are carried out in order to investigate the potential effects and reactions of these medicines on human metabolism.

To unravel the intricate connection between the molecular structure and bioactivity within a series of quinazolines, our investigation adopted a synergistic methodology that employed a genetic algorithm (GA) in tandem with four-dimensional quantitative structure-activity relationship (4D-QSAR) modeling. Rather than relying on a singular conformation, our model construction represented each compound with a set of conformers. The geometric and electronic structure attributes for every atom and bond in each molecule were computed and organized into an electron–conformational matrix of contiguity (ECMC). The electron conformational submatrix of activity (ECSA) was derived through a comparative analysis of these matrices. For the series of quinazolines, we developed a pharmacophore model based on chemical properties utilizing the EMRE software package. Employing a genetic algorithm, we identified crucial variables to predict theoretical activity. The training set, consisting of 41 compounds, was used to develop 4D-QSAR models, and their predictive capacity was evaluated by including an additional 20 compounds in the test set. The model, incorporating the top twelve parameters, exhibited satisfactory performance. To further scrutinize the contribution of each descriptor to biological activity within the EC–GA model, the E statistics technique was applied.

Compounds containing 1,2,4-triazoles and hydrazone structural frameworks have been widely studied and demonstrated to exhibit a wide range of pharmacological properties. In this work, a descriptive series of new hybrid compounds with a 1,2,4-triazole ring and a hydrazone moiety were designed and synthesized by oxidative cyclization of thiourea derivatives using molecular iodine under mild conditions. The structure of synthesized compounds was characterized by employing different spectral techniques including FTIR, ¹H NMR, ¹³C NMR, HRMS, and X-ray analysis. Furthermore, the proposed structure of BIH2 was resolved using single-crystal X-ray analysis. The compounds were evaluated for their in vitro anti-breast cancer activity and the most promising compound BIH4 with a methyl group substituted to a benzene ring exhibited significant dose-dependent cytotoxicity with selectivity to MDA-MB-231 cells in a Resazurin assay. The investigation of cell cycle progression analysis of the most active compound using a flow cytometer displayed a cell cycle arrest in the sub G1 phase. Furthermore, molecular modeling studies for the synthesized compounds were performed to predict their binding affinities toward the active site of BCL-2. The findings of the molecular modeling investigations were highly consistent with those of the cytotoxicity results.