Pharmacophore generation and atom-based 3D-QSAR of novel quinoline-3-carbonitrile derivatives as Tpl2 kinase inhibitors

Tumour progression locus-2 (Tpl2) is a serine/threonine kinase, which regulates the expression of tumour necrosis factor α. The article describes the development of a robust pharmacophore model and the investigation of structure-activity relationship analysis of quinoline-3-carbonitrile derivatives reported for Tpl2 kinase inhibition. A five point pharmacophore model (ADRRR) was developed and used to derive a predictive atom-based 3-dimensional quantitative structure activity relationship (3D-QSAR) model. The obtained 3D-QSAR model has an excellent correlation coefficient value (r(2)= 0.96), Fisher ratio (F = 131.9) and exhibited good predictive power (q(2) = 0.79). The QSAR model suggests that the inclusion of hydrophobic substituents will enhance the Tpl2 kinase inhibition. In addition, H-bond donating groups, negative ionic groups and electron withdrawing groups positively contribute to the Tpl2 kinase inhibition. Further, pharmacophoric model was validated by the receiver operating characteristic curve analysis and was employed for virtual screening to identify six potential Tpl2 kinase inhibitors. The findings of this study provide a set of guidelines for designing compounds with better Tpl2 kinase inhibitory potency.     

Pharmacophore modelling and atom-based 3D-QSAR studies on N-methyl pyrimidones as HIV-1 integrase inhibitors

Pharmacophore modelling and atom-based 3D-QSAR studies were carried out for a series of compounds belonging to N-methyl pyrimidones as HIV-1 integrase inhibitors. Based on the ligand-based pharmacophore model, we got 5-point pharmacophore model AADDR, with two hydrogen bond acceptors (A), two hydrogen bond donors (D) and one aromatic ring (R). The generated pharmacophore-based alignment was used to derive a predictive atom-based 3D-QSAR model for the training set (r²?=?0.92, SD?=?0.16, F?=?84.8, N?=?40) and for test set (Q²?=?0.71, RMSE?=?0.06, Pearson R?=?0.90, N?=?10). From these results, AADDR pharmacophore feature was selected as best common pharmacophore hypothesis, and atom-based 3D-QSAR results also support the outcome by means of favourable and unfavourable regions of hydrophobic and electron-withdrawing groups for the most potent compound 30. These results can be useful for further design of new and potent HIV-1 IN inhibitors.     

Atom-based 3D-QSAR,induced fit docking,and molecular dynamics simulations study of thieno[2,3-b]pyridines negative allosteric modulators of mGluR5

Atom-based three dimensional-quantitative structure–activity relationship (3D-QSAR) model was developed on the basis of 5-point pharmacophore hypothesis (AARRR) with two hydrogen bond acceptors (A) and three aromatic rings for the derivatives of thieno[2,3-b]pyridine, which modulates the activity to inhibit the mGluR5 receptor. Generation of a highly predictive 3D-QSAR model was performed using the alignment of predicted pharmacophore hypothesis for the training set (R²?=?0.84, SD?=?0.26, F?=?45.8, N?=?29) and test set (Q²?=?0.74, RMSE?=?0.235, Pearson-R?=?0.94, N?=?9). The best pharmacophore hypothesis AARRR was selected, and developed three dimensional-quantitative structure activity relationship (3D-QSAR) model also supported the outcome of this study by means of favorable and unfavorable electron withdrawing group and hydrophobic regions of most active compound 42d and least active compound 18b. Following, induced fit docking and binding free energy calculations reveals the reliable binding orientation of the compounds. Finally, molecular dynamics simulations for 100?ns were performed to depict the protein–ligand stability. We anticipate that the resulted outcome could be supportive to discover potent negative allosteric modulators for metabotropic glutamate receptor 5 (mGluR5).     

Pharmacophore modelling and atom-based 3D-QSAR studies on N-methyl pyrimidones as HIV-1 integrase inhibitors

Pharmacophore modelling and atom-based 3D-QSAR studies were carried out for a series of compounds belonging to N-methyl pyrimidones as HIV-1 integrase inhibitors. Based on the ligand-based pharmacophore model, we got 5-point pharmacophore model AADDR, with two hydrogen bond acceptors (A), two hydrogen bond donors (D) and one aromatic ring (R). The generated pharmacophore-based alignment was used to derive a predictive atom-based 3D-QSAR model for the training set (r(2)?=?0.92, SD?=?0.16, F?=?84.8, N?=?40) and for test set (Q(2)?=?0.71, RMSE?=?0.06, Pearson R?=?0.90, N?=?10). From these results, AADDR pharmacophore feature was selected as best common pharmacophore hypothesis, and atom-based 3D-QSAR results also support the outcome by means of favourable and unfavourable regions of hydrophobic and electron-withdrawing groups for the most potent compound 30. These results can be useful for further design of new and potent HIV-1 IN inhibitors.     

Pharmacophore based 3D-QSAR modeling and free energy analysis of VEGFR-2 inhibitors

VEGFR-2, a transmembrane tyrosine kinase receptor is responsible for angiogenesis and has been an attractive target in treating cancers. The inhibition mechanism of structurally diverse urea derivatives, reported as VEGFR-2 inhibitors, was explored by pharmacophore modeling, QSAR, and molecular dynamics based free energy analysis.The pharmacophore hypothesis AADRR, resulted in a highly significant atom based 3D-QSAR model (r² = 0.94 and q² = 0.84). Binding free energy analysis of the docked complexes of highly active and inactive compounds, after 7 ns MD simulation, revealed the importance of van der Waals interaction in VEGFR-2 inhibition. The decomposition of binding free energy on a per residue basis disclosed that the residues in hinge region and hydrophobic pocket play a role in discriminating the active and inactive inhibitors. Thus, the present study proposes a pharmacophore hypothesis representing the identified interactions pattern and its further application as a template in screening databases to identify novel VEGFR-2 inhibitor scaffolds.     

BCR-ABL tyrosine kinase inhibitor pharmacophore model derived from a series of phenylaminopyrimidine-based (PAP) derivatives

To reveal novel insights into the inhibition of BCR-ABL tyrosine kinase, pharmacophore mapping studies were performed for a series of phenylaminopyrimidine-based (PAP) derivatives, including imatinib (Gleevec). A seven-point pharmacophore model with one hydrophobic group (H), two hydrogen bond donors (D) and four aromatic rings (R) was developed using phase (pharmacophore alignment & scoring engine). The pharmacophore hypothesis yielded a statistically significant 3D-QSAR model, with a correlation coefficient of 0.886 and a survival score of 4.97 for training set molecules. The model showed excellent predictive power, with a correlation coefficient of Q² = 0.768 for an external test set of ten molecules. The results obtained from our studies provide a valuable tool for designing new lead molecules with potent activity.     

Pharmacophore modeling,comprehensive 3D-QSAR,and binding mode analysis of TGR5 agonists

Takeda G-protein-coupled receptor 5 (TGR5) is emerging as an important and promising target for the development of anti-diabetic drugs. Pharmacophore modeling and atom-based 3D-QSAR studies were carried out on a new series of 5-phenoxy-1,3-dimethyl-1H-pyrazole-4-carboxamides as highly potent agonists of TGR5. The generated best six featured pharmacophore model AAHHRR consists of two hydrogen bond acceptors (A): two hydrophobic groups (H) and two aromatic rings (R). The constructed 3D-QSAR model acquired excellent correlation coefficient value (R^2?=^?0.9018), exhibited good predictive power (Q^2?=^?0.8494) and high Fisher ratio (F?=?61.2). The pharmacophore model was validated through Guner–Henry (GH) scoring method. The GH value of 0.5743 indicated that the AAHHRR model was statistically valuable and reliable in the identification of TGR5 agonists. Furthermore, the combined approach of molecular docking and binding free energy calculations were carried out for the 5-phenoxy-1,3-dimethyl-1H-pyrazole-4-carboxamides to explore the binding mode and interaction pattern. The generated contour maps revealed the important structural insights for the activity of the compounds. The results obtained from this study could be helpful in the development of novel and more potent agonists of TGR5.     

3D-QSAR studies of checkpoint kinase 1 inhibitors based on molecular docking and CoMFA

Three-dimensional quantitative structure–activity relationship (3D-QSAR) studies were performed on a series of substituted 1,4-dihydroindeno[1,2-c]pyrazoles inhibitors, using molecular docking and comparative molecular field analysis (CoMFA). The docking results from GOLD 3.0.1 provide a reliable conformational alignment scheme for the 3D-QSAR model. Based on the docking conformations and alignments, highly predictive CoMFA model was built with cross-validated q ² value of 0.534 and non-cross-validated partial least-squares analysis with the optimum components of six showed a conventional r ² value of 0.911. The predictive ability of this model was validated by the testing set with a conventional r ² value of 0.812. Based on the docking and CoMFA, we have identified some key features of the 1,4-dihydroindeno[1,2-c]pyrazoles derivatives that are responsible for checkpoint kinase 1 inhibitory activity. The analyses may be used to design more potent 1,4-dihydroindeno[1,2-c]pyrazoles derivatives and predict their activity prior to synthesis.     

Pharmacophore modeling, 3D-QSAR,and in silico ADME prediction of N-pyridyl and pyrimidine benzamides as potent antiepileptic agents

Biological mechanism attributing mutations in KCNQ2/Q3 results in benign familial neonatal epilepsy (BFNE), a rare form of epilepsy and thus neglected. It offers a potential target for antiepileptic drug discovery. In the present work, a pharmacophore-based 3D-QSAR model was generated for a series of N-pyridyl and pyrimidine benzamides possessing KCNQ2/Q3 opening activity. The pharmacophore model generated contains one hydrogen bond donor (D), one hydrophobic (H), and two aromatic rings (R). They are the crucial molecular write-up detailing predicted binding efficacy of high affinity and low affinity ligands for KCNQ2/Q3 opening activity. Furthermore, it has been validated by using a biological correlation between pharmacophore hypothesis-based 3D-QSAR variables and functional fingerprints of openers responsible for the receptor binding and also by docking of these benzamides into the validated homology model. Excellent statistical computational tools of QSAR model such as good correlation coefficient (R^2?>^?0.80), higher F value (F?>?39), and excellent predictive power (Q²?>?0.7) with low standard deviation (SD <0.3) strongly suggest that the developed model could be used for prediction of antiepileptic activity of newer analogs. A preliminary pharmacokinetic profile of these derivatives was also performed on the basis of QikProp predictions.     

Predicting the potency of hERG K<Superscript>+</Superscript> channel inhibition by combining 3D-QSAR pharmacophore and 2D-QSAR models

Blockade of the hERG K⁺ channel has been identified as the most important mechanism of QT interval prolongation and thus inducing cardiac risk. In this work, an ensemble of 3D-QSAR pharmacophore models was constructed to provide insight into the determinants of the interactions between the hERG K⁺ channel and channel inhibitors. To predict hERG inhibitory activities, the predicted values from the ensemble of models were averaged, and the results thus obtained showed that the predictive ability of the combined 3D-QSAR pharmacophore model was greater that those of the individual models. Also, using the same training and test sets, a 2D-QSAR model based on a heuristic machine-learning method was developed in order to analyze the physicochemical characters of hERG inhibitors. The models indicated that the inhibitors have certain key inhibitory features in common, including hydrophobicity, aromaticity, and flexibility. A final model was developed by combining the combined 3D-QSAR pharmacophore with the 2D-QSAR model, and this final model outperformed any other individual model, showing the highest predictive ability and the lowest deviation. This model can not only predict hERG inhibitory potency accurately, thus allowing fast cardiac safety evaluation, but it provides an effective tool for avoiding hERG inhibitory liability and thus enhanced cardiac risk in the design and optimization of new chemical entities.     

Exploration of new scaffolds as potential MAO-A inhibitors using pharmacophore and 3D-QSAR based in silico screening

Monoamine oxidase-A (MAO-A) inhibitors are of particular importance in the treatment of depressive disorders. Herein described is pharmacophore generation and atom-based 3D-QSAR analysis of previously reported pyrrole based MAO-A inhibitors in order to get insight into their structural requirements responsible for high affinity. The best pharmacophore model generated consisted of four features DHHR: a hydrogen bond donor (D), two hydrophobic groups (H) and an aromatic ring (R). Based on model generated, a statistically valid 3D-QSAR with good predictability was developed. Derived pharmacophore was used as a query to search Zinc ‘clean drug-like’ database. Hits retrieved were passed progressively through filters like fitness score, predicted activity and docking scores. The survived hits present new scaffolds with a potential for MAO-A inhibition.     

Constrained NBMPR analogue synthesis, pharmacophore mapping and 3D-QSAR modeling of equilibrative nucleoside transporter 1 (ENT1) inhibitory activity

Conformationally constrained analogue synthesis was undertaken to aid in pharmacophore mapping and 3D-QSAR analysis of nitrobenzylmercaptopurine riboside (NBMPR) congeners as equilibriative nucleoside transporter 1 (ENT1) inhibitors. In our previous study [J. Med. Chem. 2003, 46, 831-837], novel regioisomeric nitro-1,2,3,4-tetrahydroisoquinoline conformationally constrained analogues of NBMPR were synthesized and evaluated as ENT1 ligands. 7-NO(2)-1,2,3,4-Tetrahydroisoquino-2-yl purine riboside was identified as the analogue with the nitro group in the best orientation at the NBMPR binding site of ENT1. In the present study, further conformational constraining was introduced by synthesizing 5'-O,8-cyclo derivatives. The flow cytometrically determined binding affinities indicated that the additional 5'-O,8-cyclo constraining was unfavorable for binding to the ENT1 transporter. The structure-activity relationship (SAR) acquired was applied to pharmacophore mapping using the PHASE program. The best pharmacophore hypothesis obtained embodied an anti-conformation with three hydrogen-bond acceptors, one hydrophobic center, and two aromatic rings involving the 3'-OH, 4'-oxygen, the NO(2) group, the benzyl phenyl and the imidazole and pyrimidine portions of the purine ring, respectively. A PHASE 3D-QSAR model derived with this pharmacophore yielded an r(2) of 0.916 for four (4) PLS components, and an excellent external test set predictive r(2) of 0.78 for 39 compounds. This pharmacophore was used for molecular alignment in a comparative molecular field analysis (CoMFA) 3D-QSAR study that also afforded a predictive model with external test set validation predictive r(2) of 0.73. Thus, although limited, this study suggests that the bioactive conformation for NBMPR at the ENT1 transporter could be anti. The study has also suggested an ENT1 inhibitory pharmacophore, and established a predictive CoMFA 3D-QSAR model that might be useful for novel ENT1 inhibitor discovery and optimization.     

Structural insights of JAK2 inhibitors: pharmacophore modeling and ligand-based 3D-QSAR studies of pyrido-indole derivatives

Abstract

In this study we have performed pharmacophore modeling and built a 3D QSAR model for pyrido-indole derivatives as Janus Kinase 2 inhibitors. An efficient pharmacophore has been identified from a data set of 51 molecules and the identified pharmacophore hypothesis consisted of one hydrogen bond acceptor, two hydrogen bond donors and three aromatic rings, i.e. ADDRRR. A powerful 3D-QSAR model has also been constructed by employing Partial Least Square regression analysis with a regression coefficient of 0.97 (R²) and Q² of 0.95, and Pearson-R of 0.98.     

Design,synthesis and biological evaluation of novel zanamivir derivatives as potent neuraminidase inhibitors

Neuraminidase (NA) is an important antiviral drug target. Zanamivir is one of the most potent NA inhibitors. In this paper, a series of zanamivir derivatives as potential NA inhibitors were studied by combination of molecular modeling techniques including 3D-QSAR, molecular docking, and molecular dynamics (MD) simulation. The results show that the best CoMFA (comparative molecular field analysis) model has q²?=?0.728 and r²?=?0.988, and the best CoMSIA (comparative molecular similarity indices analysis) model has q²?=?0.750 and r²?=?0.981, respectively. The built 3D-QSAR models show significant statistical quality and excellent predictive ability. Seven new NA inhibitors were designed and predicted. 20?ns of MD simulations were carried out and their binding free energies were calculated. Two designed compounds were selected to be synthesized and biologically evaluated by NA inhibition and virus inhibition assays. One compound (IC₅₀?=?0.670?µM, SI?>?149) exhibits excellent antiviral activity against A/WSN/33 H1N1, which is superior to the reference drug zanamivir (IC₅₀?=?0.873?µM, SI?>?115). The theoretical and experimental results may provide reference for development of new anti-influenza drugs.     

Molecular modeling studies,synthesis and biological evaluation of dabigatran analogues as thrombin inhibitors

In this work, 48 thrombin inhibitors based on the structural scaffold of dabigatran were analyzed using a combination of molecular modeling techniques. We generated three-dimensional quantitative structure–activity relationship (3D-QSAR) models based on three alignments for both comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) to highlight the structural requirements for thrombin protein inhibition. In addition to the 3D-QSAR study, Topomer CoMFA model also was established with a higher leave-one-out cross-validation q² and a non-cross-validation r², which suggest that the three models have good predictive ability. The results indicated that the steric, hydrophobic and electrostatic fields play key roles in QSAR model. Furthermore, we employed molecular docking and re-docking simulation explored the binding relationship of the ligand and the receptor protein in detail. Molecular docking simulations identified several key interactions that were also indicated through 3D-QSAR analysis. On the basis of the obtained results, two compounds were designed and predicted by three models, the biological evaluation in vitro (IC₅₀) demonstrated that these molecular models were effective for the development of novel potent thrombin inhibitors.     

The discovery of inhibitors of Fas-mediated cell death pathway using the combined computational method

In this study, pharmacophore and 3D-QSAR models were developed for analogues of 3-substituted-benzofuran-2-carboxylate as inhibitors of Fas-mediated cell death pathways. Our pharmacophore model has good correspondence with experimental results and can explain the variance in biological activities coherently with respect to the structure of the data set compounds. The predictive power for our synthesized compounds were 0.96 for the pharmacophore model, 0.58 for the comparative molecular field analysis (CoMFA) model, and 0.57 for the comparative molecular similarity analysis (CoMSIA) model.     

A combination of pharmacophore modeling,atom-based 3D-QSAR,molecular docking and molecular dynamics simulation studies on PDE4 enzyme inhibitors

Phosphodiesterases 4 enzyme is an attractive target for the design of anti-inflammatory and bronchodilator agents. In the present study, pharmacophore and atom-based 3D-QSAR studies were carried out for pyrazolopyridine and quinoline derivatives using Schrödinger suite 2014-3. A four-point pharmacophore model was developed using 74 molecules having pIC₅₀ ranging from 10.1 to 4.5. The best four feature model consists of one hydrogen bond acceptor, two aromatic rings, and one hydrophobic group. The pharmacophore hypothesis yielded a statistically significant 3D-QSAR model, with a high correlation coefficient (R^2?=?.9949), cross validation coefficient (Q^2?=?.7291), and Pearson-r (.9107) at six component partial least square factor. The external validation indicated that our QSAR model possessed high predictive power with R² value of .88. The generated model was further validated by enrichment studies using the decoy test. Molecular docking, free energy calculation, and molecular dynamics (MD) simulation studies have been performed to explore the putative binding modes of these ligands. A 10-ns MD simulation confirmed the docking results of both stability of the 1XMU–ligand complex and the presumed active conformation. Outcomes of the present study provide insight in designing novel molecules with better PDE4 inhibitory activity.     

Receptor based 3D-QSAR to identify putative binders of Mycobacterium tuberculosis Enoyl acyl carrier protein reductase

In the current study, the applicability and scope of 3D-QSAR models (CoMFA and CoMSIA) to complement virtual screening using 3D pharmacophore and molecular docking is examined and applied to identify potential hits against Mycobacterium tuberculosis Enoyl acyl carrier protein reductase (MtENR). Initially CoMFA and CoMSIA models were developed using series of structurally related arylamides as MtENR inhibitors. Docking studies were employed to position the inhibitors into MtENR active site to derive receptor based 3D-QSAR models. Both CoMFA and CoMSIA yielded significant cross validated q² values of 0.663 and 0.639 and r² values of 0.989 and 0.963, respectively. The statistically significant models were validated by a test set of eight compounds with predictive r² value of 0.882 and 0.875 for CoMFA and CoMSIA. The contour maps from 3D-QSAR models in combination with docked binding structures help to better interpret the structure activity relationship. Integrated with CoMFA and CoMSIA predictive models structure based (3D-pharmacophore and molecular docking) virtual screening have been employed to explore potential hits against MtENR. A representative set of 20 compounds with high predicted IC₅₀ values were sorted out in the present study.     

In silico studies on p21-activated kinase 4 inhibitors: comprehensive application of 3D-QSAR analysis,molecular docking,molecular dynamics simulations,and MM-GBSA calculation

Abstract

P21-activated kinase 4 (PAK4) is a serine/threonine protein kinase, which is associated with many cancer diseases, and thus being considered as a potential drug target. In this study, three-dimensional quantitative structure-activity relationship (3D-QSAR), molecular docking and molecular dynamics (MD) simulations were performed to explore the structure-activity relationship of a series of pyrropyrazole PAK4 inhibitors. The statistical parameters of comparative molecular field analysis (CoMFA, Q ² = 0.837, R ² = 0.990, and R ² _pred = 0.967) and comparative molecular similarity indices analysis (CoMSIA, Q ² = 0.720, R ² = 0.972, and R ² _pred = 0.946) were obtained from 3D-QSAR model, which exhibited good predictive ability and significant statistical reliability. The binding mode of PAK4 with its inhibitors was obtained through molecular docking study, which indicated that the residues of GLU396, LEU398, LYS350, and ASP458 were important for activity. Molecular mechanics generalized born surface area (MM-GBSA) method was performed to calculate the binding free energy, which indicated that the coulomb, lipophilic and van der Waals (vdW) interactions made major contributions to the binding affinity. Furthermore, through 100?ns MD simulations, we obtained the key amino acid residues and the types of interactions they participated in. Based on the constructed 3D-QSAR model, some novel pyrropyrazole derivatives targeting PAK4 were designed with improved predicted activities. Pharmacokinetic and toxicity predictions of the designed PAK4 inhibitors were obtained by the pkCSM, indicating these compounds had better absorption, distribution, metabolism, excretion and toxicity (ADMET) properties. Above research provided a valuable insight for developing novel and effective pyrropyrazole compounds targeting PAK4.