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.     

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 and docking studies of novel β-carboline derivatives as anticancer

Quantitative structure–activity relationship (QSAR) studies were performed on β-carboline derivatives for prediction of anticancer activity. The statistically significant 2D-QSAR model having r ² = 0.726 and q ² = 0.654 with pred_r ² = 0.763 was developed by stepwise multiple linear regression method. In order to understand the structural requirement of these β-carboline derivatives, a ligand-based pharmacophore 3D-QSAR model was developed. The five-point pharmacophore hypothesis yielded a 3D-QSAR model with good partial least-square statistics results (r ² = 0.73, Q _ext ²  = 0.755, F = 67.5, SD = 0.245, RMSE = 0.241, Pearson-R = 0.883). A docking study revealed the binding orientations of these derivatives at the active site amino residues of DNA intercalate (PDB ID: 1D12). The results of 2D-QSAR, atom-based 3D-QSAR, and docking studies gave detailed structural insights as well as highlighted important binding features of β-carboline derivatives as anticancer agent which provided guidance for the rational design of novel potent anticancer agents.     

Three-dimensional quantitative structure–activity relationship (3D-QSAR) analysis and molecular docking-based combined in silico rational approach to design potent and novel TRPV1 antagonists

The discovery of clinically relevant antagonists of TRPV1 for neuropathy pain therapy has proven to be a challenging task. For better understanding of the molecular interactions of antagonists with TRPV1 receptor, a series of chroman and tetrahydroquinoline ureas were analyzed by k-nearest neighbor molecular field analysis (kNN-MFA) and molecular docking. To elucidate the structural properties required for activity as TRPV1 antagonists, we report here kNN-MFA-based 3D-QSAR model for chroman and tetrahydroquinoline ureas as potent TRPV1 antagonists. Sphere exclusion method was used for dividing the compounds into training (26 compounds) and test (5 compounds) set. Overall model classification accuracy was 81.35 % (q ² = 0.8135, representing internal validation) in training set and 81.44 % (pred_r ² = 0.8144, representing external validation) in test set using stepwise forward as a method of variable selection. The stereo view of molecular rectangular grid field of 3D-QSAR using this approach showed that steric and hydrophobic effects dominantly determine binding affinities. Furthermore, the crystal structure of TRPV1 was obtained from protein data bank (PDB code 2NYJ, resolution 3.20 Å), and docking of 31 TRPV1 antagonists into putative binding sites of the TRPV1 were studies. Molecular docking was employed to explore the binding mode between these compounds and the receptor, as well as help understanding the structure–activity relationship revealed by kNN-MFA. Our QSAR model and molecular docking results corroborate with each other and propose directions for the design of new antagonists with better activity toward TRPV1.     

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 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.     

Molecular docking and 3D-QSAR studies on checkpoint kinase 1 inhibitors

Checkpoint kinase 1(Chk1) is a promising target for cancer treatment. Here three-dimensional quantitative structure–activity relationship (3D-QSAR) studies were performed on 174 1,4-dihydroindeno[1,2-c]pyrazole inhibitors of Chk1 using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). Two satisfactory ligand-based QSAR models were built (CoMFA model: q ² = 0.541, r ² = 0.880, CoMSIA model: q ² = 0.590, r ² = 0.902). The docking-based studies presented a detailed understanding of interaction between the inhibitors and Chk1. The obtained QSAR models are highly predictable (CoMFA model: q ² = 0.567, r ² = 0.891, CoMSIA model: q ² = 0.596, r ² = 0.917). The models were further validated by an external testing set obtaining $ r_{\text{pred}}^{2} $ r pred 2 values 0.896 and 0.923 for CoMFA and CoMSIA, respectively. So our models might be helpful for further modification of 1,4-dihydroindeno[1,2-c]pyrazole derivatives.     

Pharmacophore modeling, 3D-QSAR, and molecular docking study on naphthyridine derivatives as inhibitors of 3-phosphoinositide-dependent protein kinase-1

The 3-phosphoinositide-dependent protein kinase-1 (PDK1) is an imminent target for discovering novel anticancer drugs. In order to understand the structure–activity correlation of naphthyridine-based PDK-1 inhibitors, we have carried out a combined pharmacophore, three-dimensional quantitative structure–activity relationship (3D-QSAR), and molecular docking studies. The study has resulted in six point pharmacophore models with four hydrogen bond acceptors (A), one hydrogen bond donor (D), and one aromatic ring (R) are used to derive a predictive atom-based 3D-QSAR model. The generated 3D-QSAR model shows that the alignment has good correlation coefficient for the training set compounds which comprises the values of R ² = 0.96, SD = 0.2, and F = 198.2. Test set compounds shows Q ² = 0.84, RMSE = 0.56, and Pearson-R = 0.84. The external validation was carried out to validate the predicted QSAR model which shows good predictive power of $ r_{m}^{2} $  = 0.83 and k = 1.01, respectively. The external validation results also confirm the fitness of the model. The results indicated that, atom-based 3D-QSAR models and further modifications in PDK1 inhibitors via pharmacophore hypothesis are rational for the prediction of the activity of new inhibitors in prospect of drug design.     

Design of novel focal adhesion kinase inhibitors using 3D-QSAR and molecular docking

Focal adhesion kinase (FAK) plays a primary role in regulating the activity of many signaling molecules. Increased FAK expression has been implicated in a series of cellular processes, including cell migration and survival. Inhibiting the activity of FAK for cancer therapy is currently under investigation. Hence, FAK and its inhibitors have been the subject of intensive research. To understand the structural factors affecting inhibitory potency, molecular docking and 3D-QSAR modeling were studied in this project. CoMFA and CoMSIA methods were used for deriving 3D-QSAR models, which were trained with 78 compounds and then were evaluated for predictive ability with additional 19 compounds. Two substructure-based 3D-QSAR models, including CoMFA model (r ² = 0.930; q ² = 0.629) and CoMSIA model (r ² = 0.952; q ² = 0.586), had a good quality to predict the biological activities of new compounds. Meanwhile, using the flexible docking strategy, two docking-based 3D-QSAR models (CoMFA with r ² = 0.914; q ² = 0.594; CoMSIA with r ² = 0.914; q ² = 0.524) were also constructed. The structure–activity relationship has been illustrated clearly by the contour maps gained from the 3D-QSAR models in combination with the docked binding structures. All the results indicated that it might be useful in the rational design of novel FAK inhibitors.