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.     

A comprehensive structure–activity analysis 2,3,5-trisubstituted 4,5-dihydro-4-oxo-3H-imidazo [4,5-c] pyridine derivatives as angiotensin II receptor antagonists: using 2D- and 3D-QSAR approach

The QSAR studies were performed on a series of 2,3,5-trisubstituted 4,5-dihydro-4-oxo-3H-imidazo [4,5-c] pyridine derivatives as angiotensin II AT₁ receptor antagonists activity to find out the structural features requirements for the antihypertensive activity. The QSAR study was carried out on V-life Molecular Design Suite software and the derived best QSAR model by partial least square principal component regression and multiple linear regression method showed variation in biological activity. The statistically best significant model with high-correlation coefficient (r ² = 0.9425) was selected for further study and the resulted validation parameters of the model, cross-validated correlation coefficient (q ² = 0.7786 and pred_r ² = 0.8562) show the model has good predictive activities. The model showed that the parameters SdssCcount, SssNHcount, and SaaaCcount and H_Donor count are highly correlated with angiotensin II AT₁ receptor antagonists activity of 2,3,5-trisubstituted 4,5-dihydro-4-oxo-3H-imidazo [4,5-c] pyridine derivatives. Partial least square (PLS) methodology coupled with various feature selection methods viz. stepwise, simulated annealing and genetic algorithm were applied to derive 3D-QSAR models which were further validated for statistical significance and predictive ability by internal and external validation. Molecular field analysis was used to construct the best 3D-QSAR model-7 using k-nearest neighbor (kNN) method, showing good correlative and predictive capabilities in terms of q ² = 0.8316 and pred_r ² = 0.8152. Both 2D-and 3D-QSAR study of such derivatives provide guidance for further lead optimization and designing of potent anti-hypertensive agents.     

QSAR studies for some thiazolidine-2,4-dione derivatives as PIM-2 kinase inhibitors

Quantitative structure–activity relationship (QSAR) studies were performed on a series of 21 thiazolidine-2,4-dione derivatives to find the structural requirements for PIM-2 kinase inhibitory activity by two-dimensional (2D-QSAR), group-based (G-QSAR) and three-dimensional (3D-QSAR) studies. In the present study, widely used technique viz. stepwise forward–backward (SW-FB) has been applied for the development of 2D- and G-QSAR as variable selection method. The statistically significant best 2D-QSAR model was developed by partial least squares regression (PLSR) having r ² = 0.78, q ² = 0.63 with pred_r ² = 0.78. The statistically significant best G-QSAR model was developed by PLSR method having r ² = 0.89, q ² = 0.79 and pred_r ² = 0.82. The 3D-QSAR studies were performed by k-nearest neighbor molecular field analysis along with genetic algorithm method which showed q ² = 0.64 and pred_r ² = 0.94. A docking study revealed the binding orientations of these inhibitors at active site amino acid residues (PHE 43, ASP 124, ASP 182 and GLU 83) of PIM-2 enzyme (PDB ID: 3IWI). The results of this study may be useful to (medicinal) chemists to design more potent thiazolidine-2,4-dione analogs as PIM-2 kinase inhibitors.     

Molecular modeling studies of 3-acyl-2-phenylamino-1,4-dihydroquinolin-4-one derivatives as phosphatase SerB653 inhibitors

To understand the quantitative structure–activity relationships properties of 3-acyl-2-phenylamino-1,4-dihydroquinolin-4-one derivatives, and to design inhibitors of phosphatase SerB653 were developed. The statistical parameters of two-dimensional quantitative structure–activity relationship model showed it has good reliability and predictive ability (q ²?=?0.7180, F test?=?62.046, pred_r ²?=?0.7519). The best two-dimensional model suggests a chlorine atom substitution at position X⁴ and Y¹ for enhance activity. Three-dimensional quantitative structure–activity relationship was carried out using k-nearest neighbor method and showed cross-validated correlation coefficient (q ²) of 0.7484, and a predicted r ² for the external test (pred_r ²) of 0.6895 were obtained with best three-dimensional quantitative structure–activity relationship model. The influences of steric and electrostatic field effects generated by the contribution plot are analyzed. Pharmacophore approach for SerB653 inhibitor consists of hydrogen bond acceptor, hydrogen bond donor, and aromatic region. Two-dimensional quantitative structure–activity relationship and three-dimensional quantitative structure–activity relationship, pharmacophore analyses of 3-acyl-2-phenylamino-1,4-dihydroquinolin-4-one derivatives can provide more useful information and important structural insights for designing potent phosphatase SerB653 inhibitors.     

The imidazolidone analogs as phospholipase D1 inhibitors: analysis of the three-dimensional quantitative structure–activity relationship

Phospholipase D1 (PLD1) is one of the important enzymes in cell proliferation, apoptosis, and tumor progression. Quantitative structure–activity relationship (QSAR) analysis of PLD1 inhibitors was performed to obtain a good predictive model for providing structural rationale for the activity of inhibitors. The 3D-QSAR analysis was carried out on 31 imidazolidone-based analogs of PLD1 inhibitors in the training set. The molecular field analysis (MFA) with G/PLS method was used to generate a statistically significant 3D-QSAR model (r ²?=?0.930) based on molecular field generated by electrostatic and steric probes. The QSAR model was validated using leave-one-out cross-validation, bootstrapping and randomization methods, and finally with an external test set comprising nine inhibitors. The analysis of the best MFA model provided insights into possible modifications for the rational design of imidazolidone analogs as PLD1 inhibitors for better activity.     

QSAR and flexible docking studies of some aldose reductase inhibitors obtained from natural origin

Treatment of diabetic complications with inhibitors of aldose reductase (AR) is biochemically attractive because the AR initiated accumulation of sorbitol, and its resulting pathology, only appears to be significant under nonphysiological conditions of hyperglycemia. A large variety of structurally diverse compounds have been identified to date as potent in vitro AR inhibitors which are obtained from terrestrial, micro-organism and marine sources. The techniques of quantitative structure activity relationship (QSAR) and docking are valuable molecular modeling tools for drug design. In this study, 2D-, 3D-QSAR, and interaction studies of some natural AR inhibitors were carried out using VLife MDS, Schrodinger molecular modeling interface, and Molecular Virtual Docker (MVD). The developed QSAR models shown r ²?=?0.75, pred_r ²?=?0.62 with MLR analysis. Docking study revealed important interactions of natural compounds with the active binding site of AR enzyme. It is clear that the present developed QSAR model and docking study information could provide important guidelines in the design and development of possible AR inhibitors.     

Insight into structural requirements of antiamoebic flavonoids: 3D‐QSAR and G‐QSAR studies

Plant‐based flavonoids have been found to exhibit strong inhibitory capability against Entamoeba histolytica. So, various QSAR models have been developed to identify the critical features that are responsible for the potency of these molecules. 3D‐QSAR analysis using k‐nearest neighbour molecular field analysis via stepwise forward–backward variable selection method showed best results for both internal and external predictive ability of the model (i.e., q² = 0.64 and pred_r² = 0.56). Also, a group‐based QSAR (G‐QSAR) model was developed based on partial least squares regression combined with stepwise forward–backward variable selection method. It gave best parametric results (r² = 0.74, q² = 0.56 and pred_r² = 0.54) which implied that the model is highly predictive. 3D‐QSAR established that presence/absence of bulk near rings B and C is important in deciding the inhibitory potential of these molecules. Additionally, G‐QSAR provided site‐specific clue wherein modifications related to molecular weight, electronegativity and separation of an oxygen atom in rings A and C can result in enhanced biological activity. To the best of the author's knowledge, this is the first QSAR study of antiamoebic flavonoids, and therefore, we expect the results to be useful in the design of more potent antiamoebic inhibitors.