Atom based 3D-QSAR study of 1,4-benzodiazepine-2-ones as potent anti-trypanosomal agents and its validation

In the investigation, a robust pharmacophore model to investigate structure–activity relationship of 1,4-benzodiazepine-2-ones derivatives was developed for human African trypanosomiasis (HAT) using PHASE module of Schrodinger software. The pharmacophore model consists of five contours such as two aromatic rings (R), one hydrophobic hydrogen (H) and two hydrogen-bond acceptor (A) with discrete geometries. The generated pharmacophore model was used to derive a predictive atom-based three-dimensional quantitative structure–activity relationship (3D-QSAR) model for the studied data set. The obtained 3D-QSAR model has an excellent correlation coefficient value (R ² = 0.97) along with good statistical significance as shown by high Fisher ratio (F = 216.80). The model also exhibits good predictive power confirmed by the high value of cross-validated correlation coefficient (Q ² = 0.80). To confirm the validity of the model further calculation of the enrichment factor and percentage recovery studies were calculated, which confirm the validity of the model. The findings of the QSAR study provide a set of guidelines for designing compounds with better HAT inhibitory activity.     

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.     

Molecular modelling studies on flavonoid derivatives as dual site inhibitors of human acetyl cholinesterase using 3D-QSAR,pharmacophore and high throughput screening approaches

Alzheimer’s disease (AD) is a multi-factorial neurodegenerative disease that affects millions of elderly people worldwide. Due to its massive occurrence and severity, there is continuing and compelling need for the development of novel and effective drugs for improved treatment of AD. Since AD is characterized by the deficiency in cholinergic neurotransmission, acetyl cholinesterase (AChE) has been considered as a promising drug target. Herein we triggered our effort to design novel and potential inhibitors of AChE using a set of 24 flavonoid compounds having inhibitory activity against AChE. We carried out 3D-QSAR-based and pharmacophore-based identification of novel natural lead candidates. The 3D-QSAR model obtained using partial least square regression showed satisfactory parametric values (r ² = 0.8227, q ² = 0.6833 and pred-r ² = 0.7893). Amongst total 14 pharmacophore hypothesis generated the one possessing following five features: one hydrogen bond acceptor, two hydrophobic regions and two aromatic rings, was considered to be the best pharmacophore hypothesis. Above-described robust and validated 3D-QSAR and pharmacophore models were used for carrying out prospective generic prediction and virtual screening on large natural compound libraries. The screened molecules from both the approaches were subjected for further docking analysis to reveal the binding modes of actions of these ligands. All the ligands were found to bind with both catalytic and anionic subsite of AChE. The molecular insights obtained from this study will be of high value for design and development of novel drugs for AD, possessing improved binding properties and low toxicity to human.     

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.     

A combined 3D QSAR and pharmacophore-based virtual screening for the identification of potent p38 MAP kinase inhibitors: an in silico approach

p38 kinase plays a vital role in inflammation mediated by tumor necrosis factor-α and interleukin-1β pathways. Inhibition of p38 kinase provides an effective way to treat inflammatory diseases. 3D-QSAR study was performed to obtain reliable comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models for a series of p38 inhibitors with three different alignment methods (Receptor based, atom by atom matching, and pharmacophore based). Among the different alignment methods, better statistics were obtained with receptor-based alignment (CoMFA: q ² = 0.777, r ² = 0.958; CoMSIA: q ² = 0.782, r ² = 0.927). Superposing CoMFA/CoMSIA contour maps on the p38 active site gave a valuable insight to understand physical factors which are important for binding. In addition, this pharmacophore model was used as a 3D query for virtual screening against NCI database. The hit compounds were further filtered by docking and scoring, and their biological activities were predicted by CoMFA and CoMSIA models.     

A paradigm for development of novel PTP 1B inhibitors: Pharmacophore modelling, atom-based 3D-QSAR and docking studies

Inhibitors of protein tyrosine phosphatase 1B (PTP 1B) are of great interest for the development of newer therapeutics for the management of type 2 diabetes mellitus (T2DM). In order to understand structural requirement of molecules to act as PTP 1B inhibitors, ligand-based pharmacophore model, atom-based 3D-QSAR and structure-based drug design studies have been performed on a series of thiophene derivatives to correlate their molecular architecture with PTP 1B inhibitory activity. A five-point pharmacophore hypothesis with one hydrogen acceptor (A), two negative ionic (N), and two aromatic rings (R) as pharmacophoric features were developed using PHASE module of Schrödinger suite. The pharmacophore hypothesis was characterized by good PLS statistics (survival score = 3.894, the best cross validated r ² (Q ²) = 0.737, regression coefficient r ² = 0.968, Pearson-R = 0.887 and F value = 253.3). Docking studies demonstrated binding orientations of ligands in dataset with bidentate binding pockets of the enzyme and importance of hydrophobic groups. Taken together, Partial least square (PLS) generated 3D-QSAR pharmacophore and regression cubes along with structure based drug design provided a three dimensional topological view of active site that can be used for rational modification of ligands for optimal PTP 1B inhibitory activity.     

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.     

Pharmacophore modeling and 3D-QSAR studies of galloyl benzamides as potent P-gp inhibitors

P-gp transporter regulates key ADME of drugs in MDR condition. In the present work, a pharmacophore-based 3D-QSAR model was generated for a series of galloyl benzamides analogs possessing P-gp inhibitory activity. Developed pharmacophore model contains two hydrogen-bond acceptors (A), one hydrophobic (H), one hydrogen-bond donor (D) and two aromatic rings (R). These are crucial molecular fingerprints which predict binding efficacy of high-affinity and low-affinity ligands to the P-gp efflux pump. These pharmacophoric features point toward key structural requirements of galloyl benzamides for potent P-gp inhibition. Furthermore, a biological correlation 3D-QSAR variants and functional fingerprints of P-gp responsible for the receptor binding were observed. Alignment of the developed model with P-gp crystal structure indicated importance of A2 and A4 H-bond acceptor sites, which are involved in the important interactions with Glu530 and His690 residues of the active site. Excellent statistical results of QSAR model such as good correlation coefficient (r ² > 0.95), higher F value (F > 205) and excellent predictive power (Q ² > 0.6) with low standard deviation (SD < 0.2) strongly suggest that the developed model is good for the future prediction of P-gp inhibitory activity of new galloyl benzamide analogs.     

Exploring structural requirement and binding interactions of β-amyloid cleavage enzyme inhibitors using molecular modeling techniques

β-Amyloid precursor protein cleavage enzyme (BACE) has been shown to be an attractive therapeutic target to control Alzheimer’s disease (AD). Inhibition of β-secretase enzyme can prevent the deposition of Aβ (β-amyloid) peptides, which is thought to be the major cause of AD. The present study has been considered to explore 3D-QSAR, HQSAR, and pharmacophore mapping studies of BACE inhibitors. Contour maps of 3D-QSAR studies (CoMFA: R ² = 0.998, se = 0.067, Q ² = 0.765, R _pred ²  = 0.772, r _m ²  = 0.739; CoMSIA: R ² = 0.992, se = 0.125, Q ² = 0.730, R _pred ²  = 0.713, r _m ²  = 0.687) explain the importance of steric and electrostatic, along with hydrogen-bond (HB) acceptor and donor for binding affinity to BACE. HQSAR study (R ² = 0.941, se = 0.326, Q ² = 0.792, R _pred ²  = 0.713, r _m ²  = 0.709) indicates the important fragments of the molecular fingerprints that might be crucial for binding affinity. Pharmacophore space modeling (R ² = 0.937, rmsd = 0.937, Q ² = 0.935, R _pred ²  = 0.709, r _m ²  = 0.837) describes that HB acceptor, donor, hydrophobic, and steric are the important features for interaction with receptor cavity. Finally, the models are adjudged through the docking study elucidating the interactions between the receptor and the ligand, indicating the structural requirements of potent BACE inhibitors.     

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.     

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.     

3D-QSAR studies of chromone derivatives as iron-chelating agent

The metal-chelating activity of a series of 48 chromone compounds, evaluated by ferrous (Fe²⁺) chelating test, were subjected to 3D-QSAR studies using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The best CoMFA model obtained from HF/6-31G* geometry optimization and field fit alignment gave cross-validated r ² (q ²) = 0.582, non-cross-validated r ² = 0.975. The best CoMSIA model gave q ² = 0.617, non-cross-validated r ² = 0.917. The resulted CoMFA and CoMSIA contour maps proposed the Fe²⁺-chelating sites of chromone compounds compared with those of quercetin.     

Design of potent human steroid 5α-reductase inhibitors: 3D-QSAR CoMFA, CoMSIA and docking studies

3D-QSAR CoMFA, CoMSIA and docking studies were performed on a set of 4-azasteroidal human steroid 5α-reductase inhibitors. The models developed using maximal common substructure-based alignment was found to be reliable and significant with good predictive r ² value. CoMSIA model developed using combination of steric, electrostatic, hydrophobic, hydrogen bond donor and hydrogen bond acceptor features has shown r _cv ²  = 0.564 with six optimum components, r _ncv ²  = 0.945, F value = 101.196, r _Pred ²  = 0.693 and SEE = 0.209. The contour plots obtained has shown a favourable effect of bulkier groups at C-17 position. Docking studies indicates the importance of bulkier groups at C-17 position for favourable activity. The study further helps in design of potent inhibitors of the enzyme.