جستجوی مقالات مرتبط با کلیدواژه "qsar" در نشریات گروه "شیمی"

Nonsteroidal anti-inflammatory drugs (NSAIDs) constitute a medication class often utilized to mitigate pain, reduce inflammation, and lower fevers. They are commonly employed to address symptoms such as headaches, dysmenorrhea, sprains, strains, colds, flu, coronavirus, and chronic conditions like arthritis, which entail prolonged discomfort. This study employs reverse degree-based entropy measures in quantitative structure properties relationship (QSPR) analysis to study NSAIDs medications' structure. A MATLAB program aids in computing these descriptors, facilitating the prediction of pharmacological activity. The linear regression model shows a strong relationship between the calculated indices and several physicochemical parameters of NSAIDs drugs. Comparative analysis with quadratic and cubic regression models is presented. It has been found that the reverse third Zagreb entropy, the Reverse Randić entropy and the reverse hyper Zagreb entropy are the best predictors for the considered physicochemical properties. This research enhances the understanding of Lyme medication structures and their pharmacological activity prediction.

To tackle medication resistance in rheumatoid arthritis, type 1 diabetes, and Grave's disease, 32 compounds were chosen as new inhibitors of autoimmune disorders and underwent 2D-QSAR, 3D-QSAR, docking, ADMET, and molecular dynamics (MD) simulation experiments. Genetic approximation-multiple linear regression (GA-MLR) was used in the 2D-QSAR investigation. The experimental activities and those obtained by model 1 were shown to have a respectable connection (r2 = 0.7616 and q2 = 0.6327). The structure-activity relationships (SAR) were statistically studied using the 3D-QSAR technique, which produced strong statistical significance for one high predictive model, comparative molecular field analysis (CoMFA: Q2=0.785; R2=0.936; rext2= 0.818). The steric and electrostatic fields control the bioactivity, according to a thorough examination of the contour maps of the prediction models. This information is very useful in understanding the qualities that must be presented to create new and powerful inhibitors of autoimmune disorders. Through these discoveries, 70 new inhibitors with improved receptor-targeting activity were designed. The last lead compounds were compound 32 and designed compound D40, which were found by virtual screening and subsequent molecular docking. Compounds 32 and D40 have the ability to target proteins such as arginine deiminase 4 (PAD4), major histocompatibility complex (MHC) class II HLA-DQ-ALPHA chain, and thyrotropin receptor (or TSH receptor) proteins, according to the results of the MD simulation for each protein-ligand complex. Our studies suggest that compound 32 and designed compound D40 be studied in vitro and in vivo against some of the selected autoimmune disorders. The MM/GBSA binding free energies are also measured for the selected drugs. For pattern recognition, structural similarity, and hotspots binding energy prediction.

The QSAR models were developed for predicting DYRK1A biological activity (EC50) with a series of 1,5-naphthyridines derivatives as highly potent DYRK1A-dependent inducers of human β-cell replication using multiple linear regressions (MLR) as a linear method and support vector machine (SVM) as a nonlinear method. The 49 chemicals in data set were randomly partitioned into training and test subsets. For the selection of molecular descriptors, the genetic algorithm (GA) feature selection approach was used, followed by MLR and SVM. Testing the prediction abilities of the obtained models were conducted using the tests of cross-validation, Y-randomization, and an external test set. By comparing the results of GA-MLR and GA-SVM models, it is clear that GA-SVM produced better results (R2train= 0.946, Ftrain= 78.641, RMSE train= 0.203), although both models had adequate predictive quality. Using the predicted results of this study, new and potent DYRK1A inhibitors can be designed. In addition, this study provides insight into a new strategy to design diabetes drugs.

Feature selection is crucial in Quantitative Structure-Activity Relationship (QSAR) studies, enhancing learning algorithms’ performance and reducing computational costs. This study evaluates the impact of eight variable selection methods on the classification of isoform-selective ligands for Bcl-2 and Bcl-xL targets using three machine learning techniques: Supervised Kohonen Network (SKN), Support Vector Machine (SVM), and Partial Least Squares Discriminant Analysis (PLS-DA). Classification models were assessed using confusion matrix parameters, 10-fold Venetian blind cross-validation, and test sets.The results show that PLS-DA and SVM have comparable classification capabilities, outperforming SKN. However, PLS-DA occasionally leaves some ligands unassigned, making SVM a more robust and efficient choice. Despite using different variable selection methods, no clear advantage was found for any specific method, with all achieving around 70% classification accuracy in validation and test series. This suggests that the choice of variable selection method does not consistently affect outcomes across all techniques.Ensuring the reliability of selected variables involves meticulous data quality assessments, literature review, and robust cross-validation. Eliminating redundant features is essential for accurate classification models, as many physicochemical properties may be irrelevant to target bioactivity. While no single method guarantees superior models, selecting important variables is vital for extracting relevant features. This study highlights the importance of careful variable selection in QSAR studies, emphasizing its role in reducing dimensionality and improving model interpretability. Ultimately, this enhances drug discovery efficiency by identifying safer and more effective compounds, reducing time and cost.

A quantitative structure- activity relationship (QSAR) has been widely used to investigation a correlation between chemical structures of molecules to their activities. In the present study, QSAR models have been carried out on 76 camptothecin (CPT) derivatives as anticancer drugs to determine the 14N nucleus quadrupole coupling constants (QCC). These quantum chemical properties have been calculated using Density Functional Theory (DFT) and B3LYP/6-311G (d, p) method in the gas phase. A training set of 60 CPT derivatives were used to construct QSAR models and a test set of 16 compounds were used to evaluate the build models that were made using multiple linear regression (MLR) analysis. Molecular descriptors were calculated by Dragon software, and the stepwise multiple linear regression and the Genetic algorithm (GA) techniques were used to select the best descriptors and build QSAR models respectively.  QSAR models were used to delineate the important descriptors responsible for the properties of the CPT derivatives. The statistically significant QSAR models derived by GA-MLR analysis were validated by Leave-One-Out Cross-Validation (LOOCV) and external validation methods. The multicollinearity of the descriptors contributed in the models was tested by calculating the variance inflation factor (VIF) and the Durbin–Watson (DW) statistics. The predictive ability of the models was found to be satisfactory. The results of QSAR study show that quantum parameters, 2D autocorrelations and Walk and path counts  descriptors contains important structural information sufficient to develop useful predictive models for the studied activities.

QSAR investigations of Caspofungin derivatives were conducted using Multiple Linear Regression (MLR), Artificial Neural Network (ANN), and Monte Carlo Methods. The obtained results were compared and GA-ANN and ICA-MLR combinations showed the best performance according to its correlation coefficient (R2) and Root Mean Sum Square Errors (RMSE). The most important physicochemical and structural descriptors were presented and discussed. Monte Carlo method revealed that the presence of a double bond with branching, a six-member cycle, the absence of halogens, the presence of sp2 carbon connected to branching, the presence of Nitrogen and Oxygen atoms, absence of Sulphur and Phosphorus are the most important molecular features. The best Caspofungin derivative was exposed to reaction with Cu, Zn, Fe using B3lyp/6-311g/lanl2dz to investigate the stability of the formed complexes, from which the Zn complex was perceived to be the most stable one. It was concluded that QSAR study and the Monte Carlo method can lead to a more comprehensive understanding of the relation between physicochemical, structural, or theoretical molecular descriptors of drugs to their biological activities and Lipophilicity.

A study of quantitative structure-activity relationship (QSAR) is applied to a set of 24 molecules derived from diarylaniline to predict the anti-HIV-1 biological activity of the test compounds and find a correlation between the different physic-chemical parameters (descriptors) of these compounds and its biological activity, using principal components analysis (PCA), multiple linear regression (MLR), multiple non-linear regression (MNLR) and the artificial neural network (ANN). We accordingly proposed a quantitative model (non-linear and linear QSAR models), and we interpreted the activity of the compounds relying on the multivariate statistical analysis. The topological descriptors were computed with ACD/ChemSketch and ChemBioOffice14.0 programs. A correlation was found between the experimental activity and those obtained by MLR and MNLR such as (Rtrain = 0.886 ; R2train = 0.786) and (Rtrain = 0.925 ; R2train = 0.857) for the training set compounds, and (RMLR-test = 0.6) and (RMNLR-test = 0.7) for a randomly chosen test set of compounds, this result could be improved with ANN such as (R = 0.916 and R2 = 0.84) with an architecture ANN (6-1-1). To evaluate the performance of the neural network and the validity of our choice of descriptors selected by MLR and trained by MNLR and ANN, we used cross-validation method (CV) including (R = 0.903 and R2 = 0.815) with the procedure leave-one-out (LOO). The results showed that the MLR and MNLR have served to predict activities, but when compared with the results given by a 6-1-1 ANN model. We realized that the predictions fulfilled by the latter model were more effective than the other models. The statistical results indicated that this model is statistically significant and showing a very good stability towards the data variation in leave-one-out (LOO) cross validation.

The fundamental topology of the structure of chemical compounds can be better understood by the method of topological indices /numerical descriptors. Topological index depicts the chemical characteristic of a molecule in numerical form. Topological indices are used for modelling of physicochemical, biological, and pharmacokinetic properties of the compounds. It plays vital role in the QSAR/QSPR studies. Descriptor’s ability to extract information typically depends on the type of molecular representation used and the specified algorithm. These numerical values help the researchers in choosing the right compound for the drug design. Chitin and chitosan derivatives act as excellent suppressor of anti-tumour and anticancer activities in living beings. The increasing morbidity and mortality rate worldwide is correlated with two most important diseases viz., obesity and diabetes. To improve health condition and prevention of chronic diseases such as asthma, arthritis, hepatitis, gastritis, atherosclerosis etc, chitin, chitosan and their derivatives play as immune-enhancing anti-inflammatory potential. As chitin and chitosan have remarkable applications discussed above, this work pinpoints on computing a polynomial from which topological indices can be extracted for specific values of the parameters. In this work, the focus is on a type of polynomial known as M-polynomial from which various 11 degree-based TIs are derived for molecular graph of chitosan derivatives such as α, β and γ -chitins.

Dihydroorotate dehydrogenase (DHODH) is a rate-limiting enzyme in the biosynthesis of pyrimidone that catalyzes the oxidation of dihydro-orotate to orotate. Uridine-monophosphate is biosynthesized using orotate. DHODH inhibitors have demonstrated antiviral activity against Cytomegalovirus, Ebola, Influenza, Epstein-Barr virus, and Picornavirus. DHODH inhibitors' anti-SARS-CoV-2 activity is also being investigated. DHODH inhibitors (leflunomide and its metabolite teriflunomide) have been demonstrated to have anti-SARS-CoV-2 activity. In relation with the importance of this enzyme in drug designing, in present analysis, we have developed statistically robust and interpretable 2D- and 3D- Quantitative structure-activity relationship (QSAR) models based on a dataset of 92 molecules of biologically active 2-aryl-4-quinoline carboxylic acid analogues reported as DHODH inhibitors. The correlation coefficient R2 values of training set of the PLS and all five KPLS models for the respective fingerprints were obtained as 0.7091, 0.8336 (linear), 0.7586 (radial), 0.8606 (dendritic), 0.6832 (desc) and 0.7670 (molprint2D) respectively (R2 ≈ 0.9 but > 0.6), while the external validation coefficient Q2 values (Q2 > 0.6) of the test set are 0.7009, 0.7503 (linear), 0.7737 (radial), 0.8250 (dendritic), 0.6756 (desc) and 0.7533 (molprint2D) with lower values of uncertainties.

This research presents quantitative structure-activity relationship (QSAR) of half maximal inhibitory concentration (IC50 ) values of 31 different Methotrxate derivatives by employing Multiple linear regression (MLR) and artificial neural networks (ANN), simulated annealing algorithm (SA) and genetic algorithm(GA). Furthermore, CORAL software was used for multiple probability simulation of the studied derivatives. The obtained results from MLR-MLR, MLR-SA, SA-ANN, MLR-GA and GA-ANN approaches were compared and GA-ANN combination showed the best performance according to its correlation coefficient (R2) and mean sum square errors (RMSE). From Monte Carlo simulations, it was found that the presence of double bond, the presence of nitrogen and oxygen, the absence of sulphur and phosphorus and connected sp2 carbon to the ring, are the most important molecular features that affect the biological activity of the drug. It was concluded that the simultaneous application of GA-ANN and Monte Carlo methods can provide a more comprehensive understanding of the relationship between a drug's physicochemical, structural, or theoretical molecular descriptors and its biological activity, leading to accelerate the development of new drugs.

Multiple linear regression (MLR) as modeling tool and Imperialist Competitive Algorithm (ICA) as optimization techniques employed to choose the best set of descriptors and The CORAL software has been used as a tool for linear prediction of -log( IC50) (empirical negative logarithm of half of maximal inhibitory concentration) for Bortezomib derivatives. A high predictive ability was observed for the MLR-ICA model with the best number of empires/ imperialists (nEmp) 90 with root-mean-sum-square errors (RMSE) of 0.0121 and correlation coefficient (R2predict) of 0.9896 in gas phase.
The 25 data sets were randomly splitted into the training set, the calibration set, the test set in the Monte Carlo method and the number of compounds in the each set (n), correlation coefficient (R2) , cross-validated correlation coefficient (Q2), standard error(s) were calculated 13, 0.9826, 0.9780, 0.161 in training set; and n=6, R2= 0.8463 , Q2=0.7377, s=0.715 in test set in the Threshold (T) of 2 and probe of 3, respectively.
From the MLR-ICA method, it was revealed that Espm15u, R5p+, B06 [O-O], F03[N-N], F07[C-O], MATs3m, RDF125v are the most important descriptors. From Monte Carlo simulations, it was found that the presence of double bond and ring, absence of halogens are the most important molecular features affecting the biological activity of the drug.
It was concluded that simultaneous utilization of MLR-ICA and Monte Carlo method can lead to a more comprehensive understanding of the relation between physico-chemical, structural or theoretical molecular descriptors of drugs to their biological activities and facilitate designing of new drugs.

This study aimed at designing highly potent dengue virus (DENV) inhibitors targeting the NS2B-NS3 protease from 1,2-benzisothiazol-3(2H)-one-1,3,4-oxadiazole (BTZO) hybrid through quantitative-structure-activity relationship (QSAR) and subsequently structure-based design, molecular docking, and ADMET (Adsorption-Distribution-Metabolism-Excretion Toxicity) of the designed BTZO derivatives. A QSAR model was developed to correlate the biological activity with the descriptor calculated from the BTZO hybrid using multiple linear regression. The model was validated and the information from the model was used to design more potent derivatives which were evaluated through molecular docking and ADMET prediction. The QSAR model showed good statistical quality (R2Training = 0.89228, R2predicted = 0.72734 R2adjusted = 0.87074, Q2LOO = 0.81896, and cR2p = 0.8154) leading to the design of nine active BTZO derivatives with better inhibitory activity than the lead compound (7n). A binding score of -23.731, -20.210, -23.568 kcal/mol better than Panduratin and Ribavirin (-14.1715, -17.2571 kcal/mol) for compounds C-148, C-205, and C-206 respectively were obtained, including good ADMET properties. This discovery not only aided in understanding the binding manner of BTZO hybrid to the NS2B-N3 targets but also provided information for the development of active NS2B-N3 protease inhibitors.

The activity of the 25 different Carfilzomib derivatives was estimated using multiple linear regression (MLR), artificial neural network (ANN), and genetic algorithm(GA) and simulated annealing algorithm (SA) and Imperialist Competitive Algorithm (ICA) as optimization methods. The obtained results from MLR-MLR, MLR-GA, SA-ANN and GA-ANN techniques were compared and for combinations of modelling-optimization methods observed root mean sum square errors (RMSE) of 0.290, 0.0482, 0.0294, 0.0098 in gas phase, respectively (N=25). A high predictive ability was observed for the MLR-ICA model with the best number of empires/ imperialists (nEmp=50 ) and nEmp=100 with root-mean-sum-squared error (RMSE) of 0.00996 in gas phase. From the MLR-ICA method, it was revealed that RDF 075m, MATS1m, F04[N-O], O-059, F09[C-O] and Mor21p are the most important descriptors. From Monte Carlo simulations, it was found that the presence of double, absence of halogens, oxygen connected to double bond, sp2 carbon connected to double bond, double bond with ring, branching, nitrogen are the most important molecular features affecting the biological activity of the drug. It was concluded that simultaneous utilization of MLR-ICA, GA-ANN and Monte Carlo method can lead to a more comprehensive understanding of the relation between physico-chemical, structural or theoretical molecular descriptors of drugs to their biological activities and facilitate designing of new drugs.

QSAR investigations of lipophilicity (XLOGP3) and biological activity (IC50) values of some Doxazolidine derivatives were conducted using combinations of multiple linear regression (MLR) and artificial neural network (ANN) modeling methods and three different optimization techniques including simulated annealing (SA), genetic algorithm (GA) and Imperialist Competitive algorithm (ICA). In addition CORAL software was used to correlate the lipophilicity and biological activity to the structural parameters of the drugs. The obtained results were compared and GA-ANN and ICA-MLR combinations showed the best performance with regard to the correlation coefficient (R2) and root-mean-square error (RMSE). The most effective descriptors extracted from lipophilicity and biological activity studies were presented and discussed. From GA-ANN method, the most important physico-chemical descriptors were found to be minimum value in atomic Sanderson electronegativities and maximum value in Squared Moriguchi Octanol-Water partition coeff.(log P ˆ2) descriptors.ICA-MLR method suggests the maximum value in polarizibility, electrotopological state and atom van der Walls volume as the most important physicochemical descriptors.It was concluded that QSAR study and Monte Carlo method can lead to a more comprehensive understanding of the relation between physico-chemical, structural or theoretical molecular descriptors of drugs to their biological activities and Lipophilicity.

Monte Carlo and Multiple Linear Regression (MLR) and Imperialist Competitive Algorithm (ICA) were used to select the most appropriate descriptors. Examining the quality of the model by comparing the mean squared error (MSE) and correlation coefficient (R2), indicated that 140 is the most appropriate number of empires for the gas phase . In the Monte Carlo method, CORAL software was used and the data were randomly divided into training, calibration, and test subsets in three splits. The correlation coefficient (R2), cross-validated correlation coefficient (Q2) and standard error of the model were calculated to be respectively 0.9301, 0.7377, and 0.595 for the test set with an optimum threshold of 4. It was concluded that simultaneous utilization of MLR-ICA and Monte Carlo method can lead to a more comprehensive understanding of the relation between physico-chemical, structural or theoretical molecular descriptors of drugs to their biological activities and facilitate designing of new drugs.

Notable antimicrobial functionality were found with different sugar esters which were also reported to inhibit the multidrug resistant pathogens along with promising antimicrobial efficacy, and drug-likeness properties. Recent black fungus outbreak, especially in India, along with COVID-19 surmounted the death toll and worsened the conditions severely due to lack of appropriate drugs. Hence, several glucofuranose type esters 4-8 were screened against black fungus related protein (2WTP). These molecules, optimized by DFT, showed good chemical and biological reactivity values especially with pathogens along with satisfactory ADMET profiles. With the good in vitro antifungal activities these compounds were subjected for molecular docking against protein of mucormycosis’s pathogens, known as black fungus, followed by calculation of inhibition constant, binding energy, and molecular dynamics of the protein–ligand complex. Also, logpIC50 or pIC50 was calculated regarding the data for QSAR study. The molecular docking showed that 5-8 had good binding affinity (>-6.50 kcal/mol) while 7 (-8.00 kcal/mol) and 8 (-8.20 kcal/mol) possessed excellent binding affinity. The inhibition constant and binding energy of the compounds were found very lower among others with stable complexes in 5000 ns in molecular dynamics. Considering all the results, sugar ester 7 and 8 are found to have promising drug properties.

Quorum sensing (QS) is a bacterial communication mechanism that regulates the production of many pathogenic factors, including the formation of pigments and the ability to form biofilms that are essential for chronic infections. For discovering new inhibitors on the formation of biofilm formation, over 700 synthetic and natural compounds have been virtually screened against the triphenyl-LasR enzyme involved in Pseudomonas aeruginosa’s QS system. The 3D-QSAR studies revealed the relationship between the quantitative structure of compounds and their activity. The drug-like properties of compounds and effective pharmacophore features on the ligand interaction with protein were investigated by ADME and E-pharmacophore analysis. According to the obtained results, we identified compound with PubChem ID 118732838 with the glide score of -12.34 kcal/mol and oral absorption of 58.75% as the potential compound for inhibiting triphenyl-LasR protein. The study outcomes can help us to identify new drugs to inhibit biofilm formation and decrease bacterial resistance.

Density Functional Theory calculations, in the ground state of 2-Phenylbenzofuran, were carried out using the GGA-PBE, PBV86 and meta-GGA-TPSS hybrid functionals with 6-31G (d, p) as a basis set. First, theoretical calculations were performed using these functionals to obtain the stable conformer of the molecule. In addition, Mulliken population natural population and natural bond orbital analyses were calculated. The molecular electrostatic potential, band gap energies, global, local chemical reactivity descriptors and non-linear optical (NLO) properties were studied. Additionally, the NLO properties of 2-Phenylbenzofuran and those of its derivatives were investigated by GGA-PBE/6-31G (d,p) level of theory. The first-order hyperpolarizability value of all 2-Phenylbenzofuran derivatives was found within the range extending from 4.00 × 10-30 to 43.57 × 10-30 (esu). It indicated that they possess remarkable NLO properties. In addition, a multiple linear regression procedure was used to envisage the relationships between molecular descriptors and the activity of 2-Phenylbenzofuran derivatives; the quantitative structure-activity relationship (QSAR) studies were performed on them using quantum descriptors. The QSAR was applied to determine a correlation between the various physico-chemical parameters of the studied compounds and their biological activities. The statistical quality of the QSAR models was assessed using statistical parameters, i.e. R2, R2adj and R2cv.