Derive Quantitative Structure–activity Relationship Models Research Articles

Application of QSAR for the identification of key molecular fragments and reliable predictions of effects of textile dyes on growth rate and biomass values of Raphidocelis subcapitata.

The current quantitative structure-activity relationship (QSAR) study seeks to explore the underlying causes of fluctuations in growth rate and biomass of microalgae mainly due to textile dyes. The derived QSAR models cover two endpoints: ErC50 (growth rate) and EbC50 (biomass) of Raphidocelis subcapitata. In order to extract the structural features involved, multiple PLS (partial least squares) models have been developed with easy to interpret and uncomplicated 2D descriptors having proper physico-chemical meaning. These descriptors were calculated from Dragon, SiRMS, and PaDEL-descriptor software. Then, the models were developed initially using stepwise regression followed by partial least squares (PLS) regression, and the model development procedure for both the endpoints (ErC50 and EbC50) followed the stringent Organization for Economic Cooperation and Development (OECD) rules. Later on, the model validation was carried out with statistically significant and internationally accepted metrics (both internally and externally) in both the cases. Next, we have used the "Intelligent Consensus Predictor" tool (available from http://teqip.jdvu.ac.in/QSAR_Tools/DTCLab/) to test the prediction quality with an "intelligent" approach to select multiple models. The estimated prediction quality for the appropriate test sets reveals that the consensus models (CM) surpass the quality shown by individual models (IM) for both the endpoints (ErC50 and EbC50). Finally, the developed models were able to identify the major contributing features (hydrophobic units, unsaturation, saturation, electronegativity, branched atoms and charged fragments) related to aquatic toxicity of textile dyes.

QSAR studies on indole-azole Analogues using DTC tools; imidazole ring is more favorable for aromatase inhibition

Quantitative structure activity relationship (QSAR) studies have been performed on indole-azole based compounds to correlate molecular descriptors and aromatase inhibitory activity using Drug Theoretics and Cheminformatics (DTC) tools. For 2- and 3- [(aryl) (azolyl)methyl]indole derivatives (set-I), MATS4s, AATS4e, and ATSC7i showed good correlation with significant R2 and Q2 values as 0.93 and 0.83, while for 1-aryl-2-[(1H-imidazol-1-yl)methyl]-6-substituted-1H-indole derivatives (set-II), the derived QSAR model showed significant R2 and Q2 values as 0.93 and 0.82 respectively, with the descriptors AVP-0, MATS7s, minHaaCH. Electronic parameters such as high electronegativity and ionization potential have unfavourable influence on anti-aromatase activity of both series of compounds. Among the imidazole and 1, 2, 4-triazole rings, imidazole nucleus is more favourable due to its low electrostatic potential. The findings of the study might guide in future designing and development of more potent indole-azole based aromatase inhibitors.

In silico drug discovery of Acetylcholinesterase and Butyrylcholinesterase enzymes inhibitors based on Quantitative Structure-Activity Relationship (QSAR) and drug-likeness evaluation

In the last years, in order to achieve a better treatment of Alzheimer's disease (AD), much focus has been put on the development of cholinesterase (Acetylcholinesterase and Butyrylcholinesterase) inhibitor drugs. Thus, the aim of this study is to discover promising active compounds for Acetylcholinesterase and Butyrylcholinesterase enzymes inhibitors based on QSAR model and drug-likeness evaluation. In this study, a series of DL0410 and its 50 derivatives are accounted for the set up of two QSAR models. This allows an exploration of the main molecular descriptors that control the inhibitory activity of specific compounds towards cholinesterase enzymes: Acetylcholinesterase (AChE) and Butyrylcholinesterase (BuChE). Simultanerously, the models can help to predict the inhibitory activity of new compounds within its applicability domain. A Multiple Linear Regression (MLR) analysis is carried out to derive QSAR models. The results indicate that the QSAR models of Acetylcholinesterase and Butyrylcholinesterase inhibitory activity are robust and have a very good prediction capacity, testified by values of R2 equal to 0.935 and 0.895, respectively. The analysis carried out by adopting the QSAR models succeed in screening 15 potential compounds with higher biological activity. Subsequently, the investigated compounds has been subjected to the drug-likeness evaluation and reactivity study. The results show that most of the compounds do not present any bioavailability problem when administered orally. The results also allow determining the compounds that have not clearance problems, those that are the most stable and the most reactive among those tested. These findings indicate that the newly designed candidate drugs have promising potential toward AChE and BuChE enzyme inhibition and should be experimentally investigated.

QSAR and Docking Studies on Some Potential Anti-Cancer Agents to Predict their Effect on M14 Melanoma Cell Line

The global incidence of melanoma cancer is increasing rapidly and its metastatic form causes high mortality rates. The research report illustrates the development of a quantitative structure–activity relationship (QSAR) to predict the activities (pGI50) of some anti-cancer agents on melanoma cancer cell line. Subsequently, most potent compounds that showed better pGI50 activities were selected and screened via Lipinski’s rule of five for drug likeness, ADMET risk parameters and lastly docking simulation studies was performed to elucidate their binding mode. Kennard-Stone algorithm was adopted for the data division, the genetic function algorithm was employed for variable selection and multiple linear regressions method was utilized for model development. The derived QSAR model showed, respectively acceptable [( $$R^{2}$$ (0.805), $$R_{adjusted}^{2}$$ (0.773), Qcv2 (0.754) and $$R_{pred}^{2}$$ (0.703)]. The obtained cRP2 for Y-randomization is 0.649, and applicability-domain (A-D) was assessed via leveraged method. Among the screened compounds via Lipinski’s rule of five for oral bioavailability, ADMET risk filter for drug-like features and Docking simulation studies, compound 18 and 25 were identified as the best ligands having the better interactions energies (− 150.679 kcal mol−1 and − 176.246 kcal mol−1) and showed better interactions with the receptor than vemurafenib (− 147.245 kcal mol−1). Thus, the results of this research would be helpful in identification of lead molecule and optimization of novel drug.

QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIP STUDY ON THE INHIBITORY ACTIVITY OF COPPER SCHIFF BASE COMPLEXES AGAINST CANDIDA ALBICAN

Quantitative Structure Activity Relationship (QSAR) study was performed on Copper Schiff base complexes. Multiple Linear Regression analysis and genetic function algorithms was employed to derive QSAR model for better activity. The derived QSAR model having Squared Correlation Coefficient R 2 = 0.8345, Cross Validation Squared Correlation Coefficient Q 2 = 0.6681 and predicted R squared (R 2 pred ) = 0.5980. The predictive ability of the derived model was also confirmed by internal and external cross validation techniques. The QSAR model indicate that the descriptors (MATS4p) Moron autocorrelation of lag 4 weighed by polarizability,(RCI) Ring Complexity index, (G2m) 2 nd component symmetry directional WHIM index/weighted by mass, BI0 [N-O] Presence/absence of N-O at topological distance 10 and (nF) Number of Fluorine atoms plays an important role in predicting the activities against anti-candida albican. The result obtained in this study was used in designing more potent Copper Complexes as anti-candida albican agents.

QSAR Studies of amino-pyrimidine derivatives as Mycobacterium tuberculosis Protein Kinase B inhibitors

Quantitative structure activity relationship (QSAR) analysis was applied to a series of amino-pyrimidine derivatives as PknB inhibitors using a combination of various physicochemical and quantum descriptors. A multiple linear regression (MLR) procedure was used to model the relationships between molecular descriptors and the chemotherapeutic activity of the amino-pyrimidine derivatives. Good agreement between experimental and predicted activity values, obtained in the validation procedure, indicated the good quality of the derived QSAR model. The statistically significant best QSAR model has a cross validated correlation coefficient R2CV= 0.973 and external predictive ability of prediction R2 = 0.778 was developed by MLR. The proposed model has good robustness and predictability when verified by internal and external validation.

Prediction of the Toxicity of Binary Mixtures by QSAR Approach Using the Hypothetical Descriptors.

Organic compounds are often exposed to the environment, and have an adverse effect on the environment and human health in the form of mixtures, rather than as single chemicals. In this paper, we try to establish reliable and developed classical quantitative structure–activity relationship (QSAR) models to evaluate the toxicity of 99 binary mixtures. The derived QSAR models were built by forward stepwise multiple linear regression (MLR) and nonlinear radial basis function neural networks (RBFNNs) using the hypothetical descriptors, respectively. The statistical parameters of the MLR model provided were N (number of compounds in training set) = 79, R2 (the correlation coefficient between the predicted and observed activities)= 0.869, LOOq2 (leave-one-out correlation coefficient) = 0.864, F (Fisher’s test) = 165.494, and RMS (root mean square) = 0.599 for the training set, and Next (number of compounds in external test set) = 20, R2 = 0.853, (leave-one-out correlation coefficient for test set)= 0.825, F = 30.861, and RMS = 0.691 for the external test set. The RBFNN model gave the statistical results, namely N = 79, R2 = 0.925, LOOq2 = 0.924, F = 950.686, RMS = 0.447 for the training set, and Next = 20, R2 = 0.896, = 0.890, F = 155.424, RMS = 0.547 for the external test set. Both of the MLR and RBFNN models were evaluated by some statistical parameters and methods. The results confirm that the built models are acceptable, and can be used to predict the toxicity of the binary mixtures.

QSAR Study of Anthra[1,9-cd]pyrazol-6(2H)-one Derivatives as Potential Anticancer Agents Using Statistical Methods

In this study, the anticancer activity of a series of 32 molecules based on anthra[1,9-cd]pyrazol-6(2H)-one was studied by three-dimensional quantitative structure-activity relationship (QSAR) analyses: multiple linear regression (MLR), partial least squares (PLS), multiple nonlinear regression (MNLR), cross-validation analyses, and Y-randomization. A theoretical study of series was firstly studied using density functional theory (DFT) calculations at B3LYP/6-31 level of theory for employing to determine the structural parameters and electronic properties. Then the topological descriptors were computed using ACD/ChemSketch and ChemDraw 8.0 programs. The RNLM, given the descriptors obtained from the MLR and PLS, exhibited a correlation coefficient close to 0.91. The prediction models collected were confirmed by two methods of cross-validation and scrambling (or Y-randomization). The strong correlation between experimental and predicted activity values was observed, indicating the validation and good quality of the derived QSAR model.

A QSAR STUDY OF SUBSTITUTED PYRAZOLINE DERIVATIVES AS POTENTIAL ANTI-TUBERCULOSIS AGENTS

A quantitative structure–activity relationship (QSAR) of a series of substituted pyrazoline derivatives, in regard to their anti-tuberculosis activity, has been studied using the partial least square (PLS) analysis method. QSAR model development of 64 pyrazoline derivatives was carried out to predict anti-tubercular activity. Partial least square analysis was applied to derive QSAR models, which were further evaluated for statistical significance and predictive power by internal and external validation. The best QSAR model with good external and internal predictivity for the training and test set has shown cross validation (q2) and external validation (pred_r2) values of 0.7426 and 0.7903, respectively. Two-dimensional QSAR analyses of such pyrazoline derivatives provide important structural insights for designing potent antituberculosis drugs.

Organic Compounds Based on (E)-N-Aryl-2-ethene-sulfonamide as Microtubule Targeted Agents in Prostate Cancer: QSAR Study

(E)-N-Aryl-2-ethene-sulfonamide and its derivatives are potent anticancer agents; these compounds inhibit cancer cells proliferation. A study of quantitative structure-activity relationship (QSAR) has been applied on 40 compounds based on (E)-N-Aryl-2-ethene-sulfonamide, in order to predict their anticancer biological activity. The principal components analysis is used for minimizing the base matrix and the multiple linear regression (MLR) and multiple nonlinear regression have been used to design the relationships between the molecular descriptor and anticancer properties of the sulfonamide derivatives. The validation of the models MLR and MNLR has been done by dividing the dataset into training and test set, the external validation of multiple correlation coefficients was RpIC50 = 0.81 for MLR and RpIC50 = 0.91 for MNLR. The artificial neural network (ANN) showed a correlation coefficient close to 0.96, which concluded that this latter model is more effective and much better than the other models. This obtained model (ANN) has been confirmed by two methods of LOO cross-validation and scrambling (or Y-randomization). The high correlation between experimental and predicted activity values was observed, indicating the validation and the good quality of the derived QSAR model.

Chemometric and QSAR analysis of some thiadiazines as potential antifungal agents

Quantitative structure-activity relationship (QSAR) analysis has been performed in order to predict the antifungal activity of dihydroindeno and indeno thiadiazines against toxigenic fungus Aspergillus flavus. The studied compounds were classified according to their lipophilicity using the principal component analysis (PCA). The partial least square regression (PLSR) was used to distinguish the most important molecular descriptors for non-linear modeling. Artificial neural networks (ANNs) were applied for the antifungal activity prediction. The best QSAR models were validated by statistical parameters and graphical methods. High agreement between the observed and predicted antifungal activity values indicated the good quality of the derived QSAR models. The obtained QSAR-ANN models can be used to predict the antifungal activity of dihydroindeno and indeno thiadiazines and of structurally similar compounds. The modeling of the antifungal activity can contribute to the synthesis of new antifungal agents with better ability to protect food and feed from the mycotoxins.

Statistical methods and molecular docking for the prediction of thyroid hormone receptor subtype binding affinity and selectivity

The development of compounds that selectively modulate thyroid hormone action by serving as subtype-selective ligands of the thyroid hormone receptors (TRs) would be useful for clinical therapy. In the present work, quantitative structure-activity relationship (QSAR) models by adopting molecular descriptors to predict the TR binding activity were established based on a data set of TR ligands. The linear (multiple linear regression (MLR) and partial least squares regression (PLSR)) and nonlinear (support vector machine regression (SVR)) methods were employed to investigate the relationship between structural properties and binding activities. The proposed PLSR model was slightly superior to the MLR model and SVR model, as indicated by the reasonable statistical properties (TRβ: Rtr2 = 0.9594, Qte2 = 0.8091. TRα: Rtr2 = 0.9705, Qte2 = 0.8057). Additionally, molecular docking simulations were also performed to study the probable binding modes of the ligands and the TR subtype selectivity. The results indicate that substituents located in region A, region B, and region C and the orientation of these groups might result in the subtype selectivity based on the hydrogen bonding and electrostatic interactions. The derived QSAR models together with the molecular docking results have good potential in facilitating the discovery of novel TR ligands with improved activity and subtype selectivity.

Hansch Analysis of Novel Acetamide Derivatives as Highly Potent and Specific MAO-A Inhibitors.

A quantitative structure-activity relationship (QSAR) study of novel Acetamide derivatives as specific Mono amino oxidase (MAO) A inhibitory agents was performed with 28 compounds to derive QSAR models for better activity and lesser side effects. Various thermodynamic, electronic and steric parameters were calculated using Chem 3D package of molecular modeling software Chemoffice 7.0. QSAR models were generated employing sequential multiple regression method using in-house statistical program VALSTAT. The best models were selected from the various statistically significant equations. The study revealed that an increase in the bulkiness of the substituent's and molecular solvent accessible surface area is beneficial to the biological activity and the substitution of two interacting groups should be separated by more than three atoms will give better biological activity. Model also suggests that the presence of the comparatively less lipophilic group may increase MAO-A inhibitory activity and substituent that decrease the flexibility and increase rigidity of the nucleus will enhance the activity. The best QSAR model was selected, having a correlation coefficient (r) = 0.93271, coefficient of determination (r2) = 0.8509 with a standard deviation of predictivity (SDEP) = 0.31287 and cross validated squared correlation coefficient (Q2) = 0.92. The predictive ability of the selected model was also confirmed by leave one out cross validation and r2 predicted (r2 pred) was 0.764. This study may be useful in the designing of more potent substituted acetamide derivatives as specific MAOA inhibitors.

A Multi-Objective Genetic Algorithm for Outlier Removal.

Quantitative structure activity relationship (QSAR) or quantitative structure property relationship (QSPR) models are developed to correlate activities for sets of compounds with their structure-derived descriptors by means of mathematical models. The presence of outliers, namely, compounds that differ in some respect from the rest of the data set, compromise the ability of statistical methods to derive QSAR models with good prediction statistics. Hence, outliers should be removed from data sets prior to model derivation. Here we present a new multi-objective genetic algorithm for the identification and removal of outliers based on the k nearest neighbors (kNN) method. The algorithm was used to remove outliers from three different data sets of pharmaceutical interest (logBBB, factor 7 inhibitors, and dihydrofolate reductase inhibitors), and its performances were compared with those of five other methods for outlier removal. The results suggest that the new algorithm provides filtered data sets that (1) better maintain the internal diversity of the parent data sets and (2) give rise to QSAR models with much better prediction statistics. Equally good filtered data sets in terms of these metrics were obtained when another objective function was added to the algorithm (termed "preservation"), forcing it to remove certain compounds with low probability only. This option is highly useful when specific compounds should be preferably kept in the final data set either because they have favorable activities or because they represent interesting molecular scaffolds. We expect this new algorithm to be useful in future QSAR applications.

Molecular modeling studies of some thiazolidine-2,4-dione derivatives as 15-PGDH inhibitors

The quantitative structure–activity relationship (QSAR) studies were performed on a series of 34 thiazolidine-2,4-dione derivatives to find the structural features necessary for the inhibition of 15-PGDH. The multiple linear regression (MLR) and partial least square (PLS) regression coupled with stepwise (SW) feature selection methods were performed to derive QSAR models which were further validated for statistical significance and predictive ability by internal and external validation. The statistically significant 2D-QSAR model having r 2 = 0.71, 0.71 and q 2 = 0.62, 0.62 with pred_r 2 = 0.93, 0.88 was developed by SW-PLS and SW-MLR consecutively. Molecular field analysis was used to construct the best 3D-QSAR model using k-nearest neighbor (kNN) method, which showed good correlative and predictive capabilities in terms of q 2 = 0.78 and pred_r 2 = 0.74. A docking study revealed the binding orientations of these inhibitors at active site amino acid residues (ASN-91, GLY-93, GLY-12, TRY-151) of 15-PGDH enzyme (PDB ID: 2GDZ).

QSAR and docking studies on chalcone derivatives for antitubercular activity against M. tuberculosis H37Rv

In our prior studies, we reported some known antitubercular drugs (rifampicin and streptomycin) and newly synthesized chalcone derivatives (16–26) tested in vitro against Mycobacterium tuberculosis H37Rv strain. Most of the tested compounds were efficient antimycobacterial agents showing minimum inhibitory concentration values ranging from 3.5 to 30 µg mL−1. In the present work, a quantitative structure–activity relationship (QSAR) study has been performed on these active chalcone derivatives to correlate their chemical structures with their observed inhibiting activity against M. tuberculosis. A QSAR model that is able to correlate well the antitubercular activity with the chemical structures of active chalcone derivatives 16, 24, 25a, 25c, and 26 has been developed, which is potentially helpful in the design of novel and more potent antitubercular agents. The r2 and rCV2 of a newly derived QSAR model were 0.89 and 0.84, respectively. The QSAR study indicates that chemical properties, viz. heat of formation (kcal mol−1), lowest unoccupied molecular orbital energy (eV), and amine, hydroxyl, and methyl groups counts, correlate well with the activity. In silico screening results for oral bioavailability and absorption, distribution, metabolism, excretion, and toxicity compliance showed that compounds 25a, 25c, and 24 were found active similar to rifampicin and streptomycin. The docking study for the exploration of mechanism of action showed high binding affinity of active derivatives. Copyright © 2014 John Wiley & Sons, Ltd.

Prediction of tyrosinase inhibition for drug design using the genetic algorithm–multiple linear regressions

Modeling inhibition data for a series of most potent tyrosinase inhibitors for hyper-pigmentation treatment was done with quantitative structure–activity relationship (QSAR). In this report, multiple linear regression (MLR) methodology coupled with feature selection method, genetic algorithm (GA), was applied to derive QSAR models. A model with seven selected descriptors was obtained by the database that consisted of 49 compounds. The power of the model for prediction was verified with the leave-one-out and leave-group-out cross-validation test, which have values of 0.766 and 0.795, respectively. Moreover, the statistical parameters provided by the GA–MLR model (\( R_{\text{train}}^{ 2} \) = 0.85, \( R_{\text{test}}^{ 2} \) = 0.84, and \( F_{\text{train}}^{ 2} \) = 24.498) lead to a better understanding of the structural requirements of designing novel potent tyrosinase inhibitors. The study showed that the number of six-membered rings, the branching of a molecule, the van der Waals volume of atoms, the lopping centric index, the global charge transfer in a molecule and atomic masses are the main criteria in the inhibitory activity of compounds.

Three-dimensional quantitative structure activity relationship (QSAR) of cytotoxic active 3,5-diaryl-4,5-dihydropyrazole analogs: a comparative molecular field analysis (CoMFA) revisited study

In vitro antitumor evaluation of the synthesized 46 compounds of 3,5-diaryl-4,5-dihydropyrazoles against EAC cell lines and 3D QSAR study using pharmacophore and Comparative Molecular Field Analysis (CoMFA) methods were described. CoMFA derived QSAR model shows a good conventional squared correlation coefficient r2 and cross validated correlation coefficient r2cv 0.896 and 0.568 respectively. In this analysis steric and electrostatic field contribute to the QSAR equation by 70% and 30% respectively, suggesting that variation in biological activity of the compounds is dominated by differences in steric (van der Waals) interactions. To visualize the CoMFA steric and electrostatic field from partial least squares (PLS) analysis, contour maps are plotted as percentage contribution to the QSAR equation and are associated with the differences in biological activity.BackgroundPyrazole derivatives exhibit a wide range of biological properties including promising antitumor activity. Furthermore, Aldol condensation assisted organic synthesis has delivered rapid routes to N-containing heterocycles, including pyrazoles. Combining these features, the use of chalconisation-assisted processes will provide rapid access to a targeted dihydropyrazoles library bearing a hydrazino 3D QSAR study using pharmacophore and Comparative Molecular Field Analysis (CoMFA) methods were described for evaluation of antioxidant properties.ResultsChalcones promoted 1 of the 2 steps in a rapid, convergent synthesis of a small library of hydrazinyl pyrazole derivatives, all of which exhibited significant antitumor activity against Ehrlich Ascites Carcinoma (EAC) human tumor cell line comparable to that of the natural anticancer doxorubicin, as a reference standard during this study. In order to understand the observed pharmacological properties, quantitative structure-activity relationship (3D QSAR) study was initiated.ConclusionsChalcones heating provides a rapid and expedient route to a series of pyrazoles to investigate their chracterization scavenging properties. Given their favorable properties, in comparison with known anticancer, these pyrazole derivatives are promising leads for further development and optimization.

Molecular modeling studies of some substituted chalcone derivatives as cysteine protease inhibitors

The quantitative structure–activity relationship (QSAR) studies were performed on a series of 42 chalcone derivatives to find out the structural requirements of their antimalarial activities. The multiple linear regression (MLR) and partial least square (PLS) regression methods coupled with various feature selection methods, viz., stepwise (SW), genetic algorithm (GA) and simulated annealing (SA) were applied to derive QSAR models which were further validated for statistical significance and predictive ability by internal and external validation. The statistically significant 2D-QSAR model having r 2 = 0.8892 and q 2 = 0.7508 with pred_r 2 = 0.7403 was developed by SW-MLR and best Group-based QSAR (GQSAR) model having r 2 = 0.7884 and q 2 = 0.7038 with pred_r 2 = 0.7339 was developed by SW-PLS. Molecular field analysis was used to construct the best 3D-QSAR model using k-nearest neighbour method, showing good correlative and predictive capabilities in terms of q 2 = 0.6818 and pred_r 2 = 0.7708. A docking study revealed the binding orientations of these inhibitors at active site amino acid residues (Gln36, Cys39, Lys37, Asp35, Trp206) of falcipain-2 enzyme (PDB ID: 3BPF). Both QSAR and docking studies of such derivatives provide guidance for further lead optimization and designing of more potent antimalarial agents.

Insight into the structural requirement of aryltriazolinone derivatives as angiotensin II AT1 receptor: 2D and 3D-QSAR k-Nearest Neighbor Molecular Field Analysis approach

Quantitative structure–activity relationship (QSAR) studies were carried out on a series of 48 recently synthesised N2-aryltriazolinone biphenyl sulphonamides derivatives to investigate the structural requirements of their inhibitory activities against angiotensin II AT1 receptor. Partial least square (PLS) methodology coupled with various feature selection methods, viz. stepwise (SW), genetic algorithm, and simulated annealing, was applied to derive QSAR models which were further validated for statistical significance and predictive ability by internal and external validation. The statistically significant best 2D-QSAR model having correlation coefficient r2 = 0.8456 and cross-validated squared correlation coefficient q2 = 0.7359 with external predictive ability of pred_r2 = 0.8142 and pred_r2se = 0.2817 was developed by SW–PLS with the descriptors like T_O_O_2, slogp, T_N_F_4, H-acceptor count, and Chi3cluster. Molecular field analysis was used to construct the best 3D-QSAR model using SW–PLS method, showing good correlative and predictive capabilities in terms of q2 = 0.8201 and pred_r2 = 0.7894. The steric, electrostatic, and hydrophobic descriptors at the grid points S_837, S_1010, E_940, E_285, E_1143, and H_903 play an important role in the design of new molecule. The results of this study may be useful on the designing of more potent analogs as antihypertensive agents.