3D-QSAR, comparative molecular field analysis- smart region description (SRD) and fractional factorial design (FFD) (CoMFA-FFD), and comparative molecular field analysis-uninformative variable elimination-partial least square (CoMFA-UVEPLS) were conducted on 44 compounds. CoMFA-FFD and CoMFA-UVEPLS models give dependable complementary and prescient capacities; however, the CoMFA-FFD model did was to a little degree or degree better than CoMFA-UVEPLS. From the contour maps generated from the CoMFA-FFD and CoMFA-UVEPLS models, more important features were identified to boast the chemical structures that were responsible for inhibitors of the glycoprotein (GPC) of Lassa (LASV) Arenavirus. Secondly, docking was performed between the compounds and protein to predict their binding affinity. Based on the docking simulation approach, two compounds have chosen for further evaluation. The MD simulation approach was used to confirm the stability of the selected drug candidate to the target protein, which confirmed the stability of the selected lead drugs. Docking and MD simulations present comparative associations between the protein and the ligands. The MD simulations further described hydrogen bond, steric, and hydrophobic interactions on the ligand. The standard free binding energy calculation revealed that the two selected drug candidates have a significant binding affinity for GPC of LASV. The discussion points out positions on the ligands and their suggestions on protein interactions. The computational methodology utilized in this paper gives solid insights for an additional plan of molecules for inhibitors of the glycoprotein (GPC) of Lassa infection (LASV) Arenavirus.

In recent years, drug design for specific diseases has been of great importance for researchers. In this study, 3D-QSAR modeling was used on a series of 1,5-naphthyridine derivatives in order to design new DYRK1A inhibitors as anti-diabetes. After dividing the data set to the training and test sets, the training set was used to generate statistically significant CoMFA (r2cv = 0.376, r2ncv = 0.980) and CoMSIA (r2cv = 0.365, r2ncv = 0.783) models based on the common substructure-based alignment. Furthermore, a set of 9 compounds was created for testing the ability of the CoMFA and CoMSIA models to accurately predict compound activity. Also, the application of the CoMFA focus model provided better results (r2cv = 0.566, r2ncv = 0.988). The design of new analogues based on naphthyridines as DYRK1A inhibitors was carried out using the knowledge obtained from the contours of the CoMFA focus model. Contours were used to identify structural features of this series of analogs that are related to biological activity. Six new designed compounds, in this group of substances, showed stronger DYRK1A inhibitory activity.

CoMFA and CoMSIA methods were used to perform 3D quantitative structure-activity relationship (3D-QSAR) evaluation and molecular docking, of 5-HT6 receptor inhibitors. The CoMFA model performed on training set in biases of alignment with suitable statistical parameters (q2= 0.556, r2 = 0.836, F= 26.334, SEE=0.171). The best prediction for 5-HT6 receptor inhibitors was obtained by CoMFA (after focusing region) model with highest predictive ability (q2= 0.599, r2 = 0.857, F= 30.853, SEE=0.160) in biases of the same alignment. Using the same alignment, a consistent CoMSIA model was obtained (q2= 0.580, r2 = 0.752, F= 34.361, SEE=0.201) from the three combinations. To evaluate the prediction capability of the CoMFA and CoMSIA models, a test set of 9 compounds was used so that they could show the good predictive r2 values for CoMFA, CoMFA (after focusing region), and CoMSIA models, 0.554, 0.473, and 0.670, respectively. The obtained contour maps form models were used to identify the structural features responsible for the biological activity to design potent 5-HT6 receptor inhibitors. Molecular docking analysis along with the CoMSIA model could reveal the significant role of hydrophobic characteristics in increasing the inhibitors potency. Using the results, some new compounds were designed which showed the higher inhibitory activities as 5-HT6 receptor inhibitors.

The aim of the present study is to develop a three-dimensional quantitative structure-activity relationship (3D-QSAR) model with high predictive capability for a set of positive allosteric modulators of mGlu1 receptors, which act as anti-schizophrenic compounds. Computational modeling was based on comparative molecular field analysis (CoMFA), CoMFA-focused, and comparative molecular similarity indices analysis (CoMSIA) methods. The dataset consisting of 91 molecules was divided into training and test sets, and they were aligned based on the most active compound. The constructed and optimized models using the partial least squares (PLS) approach yielded satisfactory results. External predictability of the models was assessed by Leave-One-Out or Cross-Validation techniques, resulting in q2 values of 0.631, 0.653, and 0.594 for CoMFA, CoMFA- focused, and CoMSIA models, respectively. The statistical parameters obtained from the constructed models indicate the reliability of the models. Additionally, the 3D contours derived from the modeling process serve as a useful guide for designing more active compounds. Using the results of CoMFA- focused modeling, six new compounds were designed, and their pEC50 values were predicted. The designed compounds exhibited pEC50 values in the range of 8.28 to 8.58, indicating an increase in their biological activity compared to the reference compound.

Three-dimensional quantitative structure-activity relationship (3D-QSAR) techniques are useful methods for ligand-based drug design by correlating physicochemical descriptors from a set of related compounds to their known molecular activity or molecular property values. A novel clubbed triazolyl thiophene series of cdk5/p25 inhibitors were selected to establish 3D-QSAR models using Comparative molecular field analysis (CoMFA) and Comparative molecular similarity indices analysis (CoMSIA) methods. The optimum CoMFA and CoMSIA models obtained, were statistically significant with cross-validated correlation coefficients r2cv (q2) of 0.539 and 0.558, and conventional correlation coefficients (r2) of 0.980 and 0.967, respectively. A training set containing 88 molecules and a test set containing 24 molecules served to establish the QSAR models. Independent test set validated the external predictive power of both models with predicted correlation coefficients (r2pred) 0.968 and 0.945 for CoMFA and CoMSIA, respectively. Molecular docking was applied to explore the binding mode between the ligand and the receptor. The information obtained from molecular modeling studies may be helpful to design novel CDK5/P25 inhibitors with desired activity.

The anti-oxidant activities for a diverse set of flavonoids as TEAC (Trolox equivalent anti-oxidant capacity), assay were subjected to 3D-QSAR (3 dimensional quantitative structural-activity relationship) studies using CoMFA (comparative molecular field analysis) and CoMSIA (comparative molecular similarity indices analysis). The obtained results indicated superiority of CoMSIA model over CoMFA model. The best CoMSIA model is developed by using hydrogen-bond donor (H-bond donor) and electrostatic field components. This model gave the cross-validated correlation coefficient, Q2 = 0.512, correlation coefficient, R2 = 0.950, standard error of prediction, SE = 0.284, and F = 47.3, for training set, R2 = 0.922 and SE = 0.286, for test set indicating robustness and high prediction power of the developed model. The contour maps of electrostatic and H-bond donor fields of CoMSIA model provide interpretable and fruitful relationship between chemicals structure and their anti-oxidant activities, which give useful insights for designing new compounds with higher activity.

3D-QSAR has indeed established itself as a very useful component in the design of compounds with biological potential. The use of this tool will therefore make it possible to more easily target the modulations to be carried out in order to improve the inhibitory capacity of the series studied.Statistical analyses of CoMFA and CoMSIA molecular interaction field descriptors and the model validation methods they generate are presented and applied to the three-dimensional quantitative structure-activity relationships study of a series of 32 wild-type HCT 116 p53 inhibitor styrylquinolines. The selected CoMFA and CoMSIA models were generated by the partial least squares "PLS" method and all had very good internal prediction and cross-validation coefficient values Q² of 0.601 and 0.6 respectively. In view of the results obtained by the contour maps of the developed models as well as the results of molecular docking, new analogues of styrylquinoline were designed.The study of the physicochemical, pharmacokinetic and potential toxicity properties shows that the two newly predicted compounds T1 and T3 presented a better ADMET profile, in particular a good gastrointestinal absorption, compared to the most active compound taken from the literature,

Cyclin-dependent kinase 2 (CDK2) has appeared as a promising healing goal for anticancer treatments. Thus, this work seeks to predict new nominee drugs against cancer. In the present paper, a three-dimensional quantitative structure-activity relationship (3D-QSAR), molecular docking, and molecular dynamic simulation (MD) were put in to search the binding between CDK2 and Styrylquinoline inhibitor to design novel anticancer agents. The best 3D-QSAR models were performed via Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) with elevated values of Q2of 0.580, and 0.68, as well as values of R2= 0.90 and 0.91, sequentially. The forecasting ability of the models was tested by Y-randomization and external validation employing a test set of 9 molecules with a predicted determination coefficient Rtest2 of 0.99 and 0.94 for CoMFA and CoMSIA respectively. The molecular docking approach with Sybyl X.2 is conducted in this study to enhance the analysis taken out from CoMFA and CoMSIA contour maps and to afford an in silico search for the best appropriate method of inhibitor interaction within its enzyme .Furthermore, the dynamic performance and strength of the high-activity molecules were tested by performing MD simulations. This study provides leadership in to design of new potent molecules.

Recently, a new series of N-benzyl-3,6-dimethylbenzo[d]-isoxazol-5-amine derivatives and its prostate anti-cancer activity were produced and evaluated, respectively. Its compounds were perceived to have a strong inhibitory effect on the bromodomain of the related Tripartite motif-containing protein 24 (TRIM24). The 3D-QSAR study was applied utilizing the methods of Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA). This gave result to the cross-validation coefficient (Q2) values of 0.850 and 0.92, the determination coefficient (R2) values of 0.998 and 0.987, respectively. The predictive capacity of these models is based on a test set of seven molecules that generated acceptable values of coefficient of determination (R2 test) of 0.793 and 0.804, corresponding respectively to CoMFA and CoMSIA., respectively. The study used molecular docking analysis to validate 3D-QSAR methods and to explain the binding site interactions and energy between the TRIM24 bromodomain receptor and the most active ligands. Based on the results of the previous model, it was allowed for us to predict new and active compounds and its pharmacokinetic properties were verified using drug-likeness and ADMET prediction. Finally, to affirm the dynamic stability and behavior of the molecules, the most appropriate docked candidate molecules were simulated by molecular dynamics

Alzheimer’s disease (AD) is a kind of neuropsychiatric disorder that gradually degrades the mental abilities. High level activity of protein tyrosine phosphatase (PTP) results in memory function loss and Alzheimer disease. Thus, the inhibition of PTP activity can be considered as a potential target for the discovery of anti-Alzheimer drug. In this study, using computational techniques anti-alzheimer drug candidates will be designed.

The three-dimensional quantitative structure activity relationship (3D-QSAR) computational studies on PTP inhibitors were performed. Accordingly, comparative molecular field analysis (CoMFA), and comparative molecular similarity indices analysis (CoMSIA) methods were used to determine the required factors for the activity of these compounds. Distill module was applied for the alignment of molecules. A number of new active inhibitors have been proposed using the components of the CoMFA model. Molecular attachment studies were performed to investigate the inhibitory mechanism, identify bioactive conformer, and determine key interactions. Finally, ADMET studies (absorption, distribution, metabolism, digestion and toxicity) were performed on these inhibitors in a computer environment and compared with standard ranges.

The statistical parameters from the models (CoMFA: q2 =0.653,r2ncv=0.961, r2pred =0.770, and CoMSIA: q2=0.564, r2ncv = 0.933, r2pred= 0.746) indicate that the data are well fitted and have high predictive ability. Based on the information obtained from the constructed models, a novel set of tyrosine phosphatase inhibitors with new molecular frameworks have been introduced as new anti-Alzheimerchr('39')s drug candidates.

Computational techniques play a valuable role in drug design. Optimal r2pred and q2 statistical parameters led to the logical design of a number of new inhibitors of tyrosine phosphatase protein, which were introduced as new antimicrobial drug candidates.