A quantitative structure-activity relationship (QSAR) study of some diaryl urea derivatives of B-RAF inhibitors

2,4,6-Trimethoxy chalcone derivatives: an integrated study for redesigning novel chemical entities as anticancer agents through QSAR, molecular docking, ADMET prediction, and computational simulation

QSAR, an efficient and successful approach for optimizing lead compounds in drug design, was employed to study a reported series of compounds derived from 2,4,6-trimethoxy chalcone derivatives. The ability of these compounds to inhibit CDK1 was examined, with the help of QSARINS software for model development. The generated QSAR model revealed three significant descriptors, exhibiting strong correlations with impressive statistical values: cross-validation leave-one-out correlation coefficient (Q2LOO) = 0.6663, coefficient of determination (R2) = 0.7863, external validation coefficient (R2ext) = 0.7854, cross-validation leave-many-out correlation coefficient (Q2LMO) = 0.6256, Concordance Correlation Coefficient for cross-validation (CCCcv) = 0.8150, CCCtr = 0.8804, and CCCext = 0.8750. From the key structural findings and the insights gained from the descriptors, ETA_dPsi_A, WTPT-5, and GATS7s, new lead molecules were designed. The designed molecules were then evaluated for their CDK1 inhibitory activity using the three-descriptor model developed in this study. To evaluate their drug likeliness, in-silico ADMET predictions were made using Schrodinger's Software. Molecular docking was carried out to determine the interactions of designed compounds with the target protein. The designed compounds having excellent binding pocket molecular stability and anticancer effectiveness was substantiated by the findings of the molecular dynamics simulation. The results of this work point out important properties and crucial interactions necessary for efficient protein inhibition, suggesting lead candidates for further development as novel anticancer agents.Communicated by Ramaswamy H. Sarma.

In-silico studies of 2-aminothiazole derivatives as anticancer agents by QSAR, molecular docking, MD simulation and MM-GBSA approaches

Targeting Hec1/Nek2 is considered as crucial target for cancer treatment due to its significant role in cell proliferation. In pursuit of this, a series of twenty-five 2-aminothiazoles derivatives, along with their Hec1/Nek2 inhibitory activities were subjected to QSAR studies utilizing QSARINS software. The significant three descriptor QSAR model was generated, showing noteworthy statistical parameters: a correlation coefficient of cross validation leave one out (Q2LOO) = 0.7965, coefficient of determination (R2) = 0.8436, (R2ext) = 0.6308, cross validation leave many out (Q2LMO) = 0.7656, Concordance Correlation Coefficient (CCCCV = 0.8875), CCCtr = 0.9151, and CCCext = 0.0.7241. The descriptors integral to generated QSAR model include Moreau-Broto autocorrelation, which represents the spatial autocorrelation of a property along the molecular graph's topological structure (ATSC1i), Moran autocorrelation at lag 8, which is weighted by charges (MATS8c) and RPSA representing the total molecular surface area. It was noted that these descriptors significantly influence Hec1/Nek2 inhibitory activity of 2-aminothiazoles derivatives. New lead molecules were designed and predicted for their Hec1/Nek2 inhibitory activity based on the developed three descriptor model. Further, the ADMET and Molecular docking studies were carried out for these designed molecules. The three molecules were selected based on their docking score and further subjected for MD simulation studies. Post-MD MM-GBSA analysis were also performed to predicted the free binding energies of molecules. The study helped us to understand the key interactions between 2-aminothiazoles derivatives and Hec1/Nek2 protein that may be necessary to develop new lead molecules against cancer.Communicated by Ramaswamy H. Sarma.

Identification of a Family of Glycoside Derivatives Biologically Active against Acinetobacter baumannii and Other MDR Bacteria Using a QSPR Model

As the rate of discovery of new antibacterial compounds for multidrug-resistant bacteria is declining, there is an urge for the search for molecules that could revert this tendency. Acinetobacter baumannii has emerged as a highly virulent Gram-negative bacterium that has acquired multiple resistance mechanisms against antibiotics and is considered of critical priority. In this work, we developed a quantitative structure-property relationship (QSPR) model with 592 compounds for the identification of structural parameters related to their property as antibacterial agents against A. baumannii. QSPR mathematical validation (R2 = 70.27, RN = −0.008, a(R2) = 0.014, and δK = 0.021) and its prediction ability (Q2LMO = 67.89, Q2EXT = 67.75, a(Q2) = −0.068, δQ = 0.0, rm2¯ = 0.229, and Δrm2 = 0.522) were obtained with different statistical parameters; additional validation was done using three sets of external molecules (R2 = 72.89, 71.64 and 71.56). We used the QSPR model to perform a virtual screening on the BIOFACQUIM natural product database. From this screening, our model showed that molecules 32 to 35 and 54 to 68, isolated from different extracts of plants of the Ipomoea sp., are potential antibacterials against A. baumannii. Furthermore, biological assays showed that molecules 56 and 60 to 64 have a wide antibacterial activity against clinically isolated strains of A. baumannii, as well as other multidrug-resistant bacteria, including Staphylococcus aureus, Escherichia coli, Klebsiella pneumonia, and Pseudomonas aeruginosa. Finally, we propose 60 as a potential lead compound due to its broad-spectrum activity and its structural simplicity. Therefore, our QSPR model can be used as a tool for the investigation and search for new antibacterial compounds against A. baumannii.

QSAR analysis of pyrimidine derivatives as VEGFR-2 receptor inhibitors to inhibit cancer using multiple linear regression and artificial neural network

BACKGROUND AND PURPOSE

In this study, the pharmacological activity of 33 compounds of furopyrimidine and thienopyrimidine as vascular endothelial growth factor receptor 2 (VEGFR-2) inhibitors to inhibit cancer was investigated. The most important angiogenesis inducer is VEGF endothelial growth factor, which exerts its activity by binding to two tyrosine kinase receptors called VEGFR-1 and VEGFR-2. Due to the critical role of VEGF in the pathological angiogenesis of this molecule, it is a valuable therapeutic target for anti-angiogenesis therapies.

EXPERIMENTAL APPROACH

After calculating descriptors using SPSS software and stepwise selection method, 5 descriptors were used for modeling in multiple linear regression (MLR) and artificial neural network (ANN). The calibration series and the test series in this study included 26 and 7 combinations, respectively.

FINDINGS/RESULTS

The performance evaluation of models was determined by the R2, RMSE, and Q2 statistic parameters. The R2 values of MLR and ANN models were 0.889 and 0.998, respectively. Also, the value of RMSE in the ANN model was lower and its Q2 value was higher than the MLR model.

CONCLUSION AND IMPLICATIONS

The results were evaluated by different statistical methods and it was concluded that the nonlinear neural network method is powerful to predict the pharmacological activity of similar compounds, and because of the complex and nonlinear relationships, the MLR was not capable of establishing a good model with high predictive power.

Quinazoline analogues as cytotoxic agents; QSAR, docking, and in silico studies

Background and purpose:

Synthesis and investigation of pharmacological activity of novel compounds are time and money-consuming. However, computational techniques, docking, and in silico studies have facilitated drug discovery research to design pharmacologically effective compounds.

Experimental approach:

In this study, a series of quinazoline derivatives were applied to quantitative structure-activity relationship (QSAR) analysis. A collection of chemometric methods were conducted to provide relations between structural features and cytotoxic activity of a variety of quinazoline derivatives against breast cancer cell line. An in silico-screening was accomplished and new impressive lead compounds were designed to target the epidermal growth factor receptor (EGFR)-active site based on a new structural pattern. Molecular docking was performed to delve into the interactions, free binding energy, and molecular binding mode of the compounds against the EGFR target.

Findings/Results:

A comparison between different methods significantly indicated that genetic algorithm-partial least-squares were selected as the best model for quinazoline derivatives. In the current study, constitutional, functional, chemical, resource description framework, 2D autocorrelation, and charge descriptors were considered as significant parameters for the prediction of anticancer activity of quinazoline derivatives. In silico screening was employed to discover new compounds with good potential as anticancer agents and suggested to be synthesized. Also, the binding energy of docking simulation showed desired correlation with QSAR and experimental data.

Conclusion and implications:

The results showed good accordance between binding energy and QSAR results. Compounds Q₁-Q₃₀ are desired to be synthesized and applied to in vitro evaluation.

Synthesis and characterization of some novel diaryl urea derivatives bearing quinoxalindione moiety

Diaryl urea derivatives have exhibited a broad spectrum of biochemical effects and pharmaceutical applications. Several diaryl urea derivatives such as sorafenib, regorafenib, linifanib, and tivozanib and lenvatinib are in clinical trial or clinical use. Therefore, development of small molecules within the diaryl urea scaffold with the ability of binding to variety of enzymes and receptors in the biological system are an interesting topic for researchers. Sorafenib as a diaryl urea derivative is a well-known anticancer agent. Corresponding to available information about biological activities of quinoxaline moieties, based on sorafenib scaffold, several structures were designed by replacement of pyridyl carboxamide group of sorafenib with quinoxalindione moiety. A total of 14 novel compounds in 7 synthetic steps were synthesized. Briefly, the amino group of p-aminophenol was first protected followed by O-arylation of 4-acetamidophenol with 5-chloro-2-nitroaniline to provide 5-(4-acetamidophenoxy)-2-nitroaniline. Reduction of the nitro group of 5-(4-acetamidophenoxy)-2-nitroaniline and cyclization of diamine N-(4-(3,4-diaminophenoxy) phenyl) acetamides with oxalic acid afforded compound N-(4-((2,3-dioxo-1,2,3,4-tetrahydroquinoxalin-6-yl)oxy)phenyl) acetamides which on deacetylation gave compounds 6-(4-aminophenoxy) quinoxaline-2,3 (1H, 4H)-diones. Then resultant compounds, 6-(4-aminophenoxy) quinoxaline-2,3 (1H, 4H)-diones were reacted by appropriate isocyanates/ carbamates to give the target compounds 1-(4-((2,3-dioxo-1,2,3,4-tetrahydroquinoxalin-6-yl)oxy)phenyl)-3-phenylureas. The structures of compounds confirmed by proton nuclear magnetic resonance (1H NMR), mass spectrum and Fourier transform infrared (FT-IR).

Biological evaluation, docking and molecular dynamic simulation of some novel diaryl urea derivatives bearing quinoxalindione moiety

In this study a series of diarylurea derivatives containing quinoxalindione group were biologically evaluated for their cytotoxic activities using MTT assay against MCF-7 and HepG2 cell lines. Antibacterial activities of these compounds were also evaluated by Microplate Alamar Blue Assay (MABA) against three Gram-negative (Escherichia coli, Pseudomonas aeruginosa and Salmonella typhi), three Gram-positive (Staphylococcus aureus, Bacillus subtilis and Listeria monocitogenes) and one yeast-like fungus (Candida albicans) strain. Furthermore, molecular docking was carried out to study the binding pattern of the compounds to the active site of B-RAF kinase (PDB code: 1UWH). Molecular dynamics simulation was performed on the best ligand (16e) to investigate the ligand binding dynamics in the physiological environment. Cytotoxic evaluation revealed the most prominent cytotoxicity for 6 compounds with IC₅₀ values of 10-18 μM against two mentioned cell lines. None of the synthesized compounds showed significant antimicrobial activity. The obtained results of the molecular docking study showed that all compounds fitted in the binding site of enzyme with binding energy range of -11.22 to -12.69 kcal/mol vs sorafenib binding energy -11.74 kcal/mol as the lead compound. Molecular dynamic simulation indicated that the binding of ligand (16e) was stable in the active site of B-RAF during the simulation.