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Objectives: By 2030, prostate cancer is estimated to account for 1.7 million new cases and 499,000 deaths. The objectives of this research were to create a model revealing the activity of thiosemicarbazone-indole compounds as anticancer agents against the PC3 cell line; perform docking analysis between the compounds and the target enzyme; and predict the pharmacokinetics and drug-likeness of the compounds under investigation., Methods: The quantitative structureactivity relationship (QSAR) method was used to build the model; molecular docking between the compounds and the target enzyme was performed; and the drug-likeness and pharmacokinetics of the inhibiting compounds was examined., Results: The genetic function algorithm-multilinear regression approach was used for building the QSAR model. Build model 1 had the best performance, with R 2 (coefficient of determination) = 0.972517, R adj (adjusted R-squared) = 0.964665, (CRp 2 ) = 0.780922, and LOF (leave-one-out cross-validation) = 0.076524, demonstrated strongly indicated by the molecular descriptors. SHBd, SsCH3, JGI2, and RDF60P were highly dependent on proliferative activity. Compounds ID 7 and 22 had the potential to act as androgen receptor inhibitors, as suggested by molecular docking studies between the drugs and their target enzymes. Compounds ID 7 and 22 exhibited binding scores of -8.5 kcal/mol and -8.8 kcal/mol, respectively. The approved maximum medication molecules for oral bioavailability included the molecules with IDs 7 and 22., Conclusion: This research provides valuable insights into the relationships among molecular descriptors, potential inhibitors, and pharmacokinetic properties in the treatment of PC3. These findings may contribute to the understanding and potential development of new therapeutic options for prostate cancer patients., (© 2024 The Authors.)

Previous studies have shown that 2-arylbenzimidazole derivatives have a strong anti-diabetic effect. To further explore this potential, we develop new analogues of the compound using ligand-based drug design and tested their inhibitory and binding properties through QSAR analyses, molecular docking, dynamic simulations and pharmacokinetic studies. By using quantitative structure activity relationship and ligand-based modification, a highly precise predictive model and design of potent compounds was developed from the derivatives of 2-arylbenzimidazoles. Molecular docking and simulation studies were then conducted to identify the optimal binding poses and pharmacokinetic profiles of the newly generated therapeutic drugs. DFT was employed to optimize the chemical structures of 2-arylbenzimidazole derivatives using B3LYP/6-31G* as the basis set. The model with the highest R 2 trng set , R 2 adj , Q 2 cv , and R 2 test sets (0.926, 0.912, 0.903, and 0.709 respectively) was chosen to predict the inhibitory activities of the derivatives. Five analogues designed using ligand-based strategy had higher activity than the hit molecule. Additionally, the designed molecules had more favorable MolDock scores than the hit molecule and acarbose and simulation studies confirm on their stability and binding affinities towards the protein. The ADME and druglikeness properties of the analogues indicated that they are safe to consume orally and have a high potential for total clearance. The results of this study showed that the suggested analogues could act as α-amylase inhibitors, which could be used as a basis for the creation of new drugs to treat type 2 diabetes mellitus., Competing Interests: Conflict of interestThe authors have no conflict of interest to declare., (© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

Background: Breast cancer is the most common tumor among females globally. Its prevalence is growing around the world, and it is alleged to be the leading cause of cancer death. Approved anti-breast cancer drugs display several side effects and resistance during the early treatment stage. Hence, there is a need for the development of more effective and safer drugs. This research was aimed at designing more potent quinazolin-4(3H)-one molecules as breast cancer inhibitors using a ligand-based design approach, studying their modes of interaction with the target enzyme using molecular docking simulation, and predicting their pharmacological properties., Methods: The QSAR model was developed using a series of quinazoline-4(3H)-one derivatives by utilizing Material Studio v8.0 software and validated both internally and externally. Applicability domain virtual screening was utilized in selecting the template molecule, which was structurally modified to design more potent molecules. The inhibitive capacities of the design molecules were predicted using the developed model. Furthermore, molecular docking was performed with the EGFR target active site residues, which were obtained from the protein data bank online server (PDB ID: 2ITO) using Molegro Virtual Docker (MVD) software. SwissADME and pkCSM online sites were utilized in predicting the pharmacological properties of the designed molecules., Results: Four QSAR models were generated, and the first model was selected due to its excellent internal and external statistical parameters as follows: R 2  = 0.919, R 2 adj  = 0.898, Q 2 cv  = 0.819, and R 2 pred  = 0.7907. The robustness of the model was also confirmed by the result of the Y-scrambling test performed with cR 2 p = 0.7049. The selected model was employed to design seven molecules, with compound 4 (pIC 50  = 5.18) adopted as the template. All the designed compounds exhibit better activities ranging from pIC 50  = 5.43 to 5.91 compared to the template and Doruxybucin (pIC 50  = 5.35). The results of molecular docking revealed better binding with the EGFR target compared with the template and Doruxybucin. The designed compounds exhibit encouraging therapeutic applicability, as evidenced by the findings of pharmacological property prediction., Conclusions: The designed derivatives could be utilized as novel anti-breast cancer agents., (© 2023. The Author(s).)

Objectives: Breast tumor is ranked as the most common tumor type identified among women globally with over 1.7 million cases annually, representing 11.9% of the total number of cancer cases. Approved anti-breast tumor drugs exhibit several side effects and some patients develop resistance during the early treatment stage. This study aimed to use an in-silico approach to identify and design potential therapeutic agents., Methods: Robust 3D-QSAR models were developed using quinazoline-4(3H)-one analogs as EGFR inhibitors. The best model was then selected based on statistical parameters and was subsequently used to design more potent therapeutic agents. Molecular docking simulation was executed using the data set and the designed compounds to identify lead compounds which were further screened by pharmacokinetic profiling by applying SwissADME and pkCSM software., Results: Internal validations of the best CoMFA and CoMSIA models (R 2  = 0.855 and 0.895; Q 2  = 0.570 and 0.599) passed the threshold values for the establishment of a consistent QSAR model. The constructed models were further validated externally using six compounds as a test set, thus revealing a satisfactory predicted correlation coefficient (R 2 pred  = 0.657 and 0.681). The CoMSIA_SHE models with the best statistical parameters were further subjected to applicability domain checks and only three influentials were detected. These were then utilized to design five novel compounds with activities ranging from 5.62 to 6.03. Molecular docking studies confirmed that compounds 20 to 26, with docking scores ranging from -163.729 to -169.796, represented lead compounds with higher docking scores compared to Gefitinib (-127.495). Furthermore, the designed compounds exhibited better docking scores ranging from -171.379 to -179.138., Conclusions: Pharmacological studies identified compounds 20, 24 26 and the designed compounds 2, 3, 5 as feasible drug candidates. However, these theoretical findings should now be validated experimentally., (© 2023 [The Author/The Authors].)

PIP4K2A is a type II lipid kinase that catalyzed the rate-limiting step of the conversion of phosphatidylinositol-5-phosphate (PI5P) into phosphatidylinositol 4,5-bisphosphate (PI4,5P2). PIP4K2A has been intricately linked to the inhibition of various types of tumors via reactive oxygen species-mediated apoptosis, making it an important therapeutic target. In the quest of finding biologically active substances with efficient PIP4K2A inhibitory activity, machine learning algorithms were used to investigate the quantitative relationship between structures and inhibitory activities of 1,7-naphthyridine analogues. Three machine learning algorithms (MLR, ANN, and SVM) were used to develop QSAR models that can effectively predict the PIP4K2A inhibitory activity of a library of 1,7-naphthyridine analogues. The cascaded feature selection method was performed by sequential application of GFA and MP5 algorithms to identify a molecular descriptor subset that can best describe the PIP4K2A inhibitory activity of 1,7-naphthyridine analogues. PIP4K2A inhibitory activities predicted by the ML models were strongly correlated with the experimental values. The QSAR Modelling indicates that the best-performing ML model was SVM with the RBF kernel function. The SVM model performed very well in predicting PIP4K2A inhibitory activity of the 1,7-naphthyridine analogues with RTR and QEX values of 0.9845 and 0.8793 respectively. To further gain more structural insight into the origin of PIP4K2A inhibitory activity of 1,7-naphthyridine analogues, molecular docking studies were performed. The results indicate that five compounds; 15, 25, 13, 09, and 28 were found to have a high binding affinity with the receptor molecules. Hydrogen bonding, pi-pi interaction, and pi-cation interactions were found to modulate the binding interaction of the inhibitors. Although the SVM gives essentially a black-box model which cannot be readily interpreted, using SVM in tandem with MLR and ANN provides a unique perspective in building robust QSAR predictive models. The superior predictive performance of the ML models and the explanatory power of MLR models were combined to provide a unique insight into the structure-activity relationship of 1,7-naphthyridine inhibitors. This is relevant in that it provides information that can be invaluable as guidelines for the design of novel PIP4K2A inhibitors., Competing Interests: The author(s) declared no potential conflicts of interest concerning the research, authorship, and/or publication of this article., (This journal is © The Royal Society of Chemistry.)

Corrosion prevention has been a global phenomenon, particularly in metallic and construction engineering. Most inhibitors are expensive and toxic. Therefore, developing nontoxic and cheap corrosion inhibitors has been a way forward. In this work, L-arginine was successfully grafted on chitosan by the thermal technique using a reflux condenser. This copolymer was characterized by Fourier-transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), and X-ray diffraction (XRD). The corrosion inhibition performance of the composite polymer was tested on mild steel in 0.5M HCl by electrochemical methods. The potentiodynamic polarization (PDP) and electrochemical impedance spectroscopy (EIS) results were consistent. The inhibition efficiency at optimum concentration rose to 91.4%. The quantum chemical calculation parameters show good properties of the material as a corrosion inhibitor. The molecular structure of the inhibitor was subjected to density functional theory (DFT) to understand its theoretical properties, and the results confirmed the inhibition efficiency of the grafted polymer for corrosion prevention.

A dataset of seventy-two (72) cytotoxic compounds of the National Cancer Institute (NCI) was studied by QSAR and docking approaches to gain deeper insights into ligands selectivity on SK-MEL-2 cell line. The QSAR model was built using fifty (50) molecules and the best-generated model based on multiple linear regression showed, respectively good quality of fits ( R 2 (0.864), R a d j u s t e d 2 (0.845), Q 2 cv (0.799) and R p r e d 2 (0.706)). The model's predictive ability was determined by a test set of twenty-two (22) compounds. Compounds 30 and 41 were selected as templates for in silico design because they had high pGI 50 activity and are in the model's applicability domain. The obtained information from the model was explored to design novel molecules by introducing various modifications. Moreover, the designed compounds with better-predicted activity (pGI 50 ) values were selected and docked on the active site of the protein (PDB-CODE: 3OG7) which is responsible for melanoma cancer to elucidate their binding mode. AN2 (-12.1kcalmol -1 ) and AC4 (-12.4kcalmol -1 ) showed a better binding score for the target when compared with (vemurafenib, -11.3kcalmol -1 ) the known inhibitor of the target (V600E-BRAF). These findings may be very helpful in early anti-cancer drug development., (© 2020 The Author(s).)