3D-QSAR studies on thieno[3,2-d]pyrimidines as phosphodiesterase IV inhibitors

Cyclic nucleotide phosphodiesterase IV (PDE IV) inhibitors find utility in asthma and Chronic Obstructive Pulmonary Disease (COPD) therapy. A series of 29 thieno[3,2-d]pyrimidines with affinity for PDE IV was subjected to three dimensional quantitative structure activity relationship (3D-QSAR) studies using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). Both CoMFA and CoMSIA provided statistically valid models with good correlative and predictive power. The incorporation of hydrophobic, hydrogen bond donor and hydrogen bond acceptor fields showed insignificant improvement in CoMSIA model. The 3D-QSAR models provide information for predicting the affinity of related compounds and designing more potent inhibitors.     

Predicting anti-cancer activity of quinoline derivatives: CoMFA and CoMSIA approach

The 3D quantitative structure-activity relationships of 31 quinoline nuclei containing compounds and their biological activity have been investigated to establish various models. The comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) studies resulted in reliable and significant computational models. The obtained CoMFA model showed high predictive ability with q(2) = 0.592, r(2) = 0.966 and standard error of estimation (SEE) = 0.167, explaining majority of the variance in the data with two principal components. Predictions obtained with CoMSIA steric, electrostatic, hydrophobic, hydrogen-bond acceptor and donor fields (q(2) = 0.533, r(2) = 0.985) showed high prediction ability with minimum SEE (0.111) and four principal components. The information obtained from the CoMFA and CoMSIA contour maps can be utilized for the design and development of topoisomerase-II inhibitors for synthesis.     

Two- and three-dimensional quantitative structure-activity relationships for a series of purine nucleoside phosphorylase inhibitors

Comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis, and hologram quantitative structure-activity relationship (HQSAR) studies were conducted on a series of 52 training set inhibitors of calf spleen purine nucleoside phosphorylase (PNP). Significant cross-validated correlation coefficients (CoMFA, q(2)=0.68; CoMSIA, q(2)=0.66; and HQSAR, q(2)=0.70) were obtained, indicating the potential of the models for untested compounds. The models were then used to predict the inhibitory potency of 16 test set compounds that were not included in the training set, and the predicted values were in good agreement with the experimental results. The final QSAR models along with the information gathered from 3D contour and 2D contribution maps should be useful for the design of novel inhibitors of PNP having improved potency.     

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.     

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.     

3D-QSAR studies on malonyl coenzyme A decarboxylase inhibitors

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on a series of Malonyl Co-A decarboxylase (MCD) inhibitors (Cheng et al. J. Med. Chem.2006, 49, 1517-1525 and Cheng et al. Bioorg. Med. Chem. Lett.2006, 16, 695-700). These inhibitors have shown protective action on the ischemic heart by inhibiting fatty acid oxidation. The CoMFA model produced statistically significant results, with the cross-validated and conventional correlation coefficients being 0.544 and 0.986, respectively. The best results were obtained by combining steric, electrostatic, hydrophobic, and H-bond acceptor fields in CoMSIA, in which case the respective cross-validated and conventional correlation coefficients were 0.551 and 0.918. The predictive ability of CoMFA and CoMSIA, determined using a test set of 24 compounds, gave predictive correlation coefficients of 0.718 and 0.725, respectively. The information obtained from CoMFA and CoMSIA 3D contour maps may be of utility in the design of more potent MCD inhibitors.     

Molecular docking and 3D-QSAR studies on β-phenylalanine derivatives as dipeptidyl peptidase IV inhibitors

Three-dimensional quantitative structure-activity relationship (3D-QSAR) and molecular docking studies were carried out to explore the binding of 73 inhibitors to dipeptidyl peptidase IV (DPP-IV), and to construct highly predictive 3D-QSAR models using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The negative logarithm of IC₅₀ (pIC₅₀) was used as the biological activity in the 3D-QSAR study. The CoMFA model was developed by steric and electrostatic field methods, and leave-one-out cross-validated partial least squares analysis yielded a cross-validated value (r_cv² {\hbox{r}}_{{\rm{cv}}}^{\rm{2}} ) of 0.759. Three CoMSIA models developed by different combinations of steric, electrostatic, hydrophobic and hydrogen-bond fields yielded significant r_cv² {\hbox{r}}_{{\rm{cv}}}^{\rm{2}} values of 0.750, 0.708 and 0.694, respectively. The CoMFA and CoMSIA models were validated by a structurally diversified test set of 18 compounds. All of the test compounds were predicted accurately using these models. The mean and standard deviation of prediction errors were within 0.33 and 0.26 for all models. Analysis of CoMFA and CoMSIA contour maps helped identify the structural requirements of inhibitors, with implications for the design of the next generation of DPP-IV inhibitors for the treatment of type 2 diabetes.     

Comparative QSAR studies on peptide deformylase inhibitors

Comparative quantitative structure–activity relationship (QSAR) analyses of peptide deformylase (PDF) inhibitors were performed with a series of previously published (British Biotech Pharmaceuticals, Oxford, UK) reverse hydroxamate derivatives having antibacterial activity against Escherichia coli PDF, using 2D and 3D QSAR methods, comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), and hologram QSAR (HQSAR). Statistically reliable models with good predictive power were generated from all three methods (CoMFA r ² = 0.957, q ² = 0.569; CoMSIA r ² = 0.924, q ² = 0.520; HQSAR r ² = 0.860, q ² = 0.578). The predictive capability of these models was validated by a set of compounds that were not included in the training set. The models based on CoMFA and CoMSIA gave satisfactory predictive r ² values of 0.687 and 0.505, respectively. The model derived from the HQSAR method showed a low predictability of 0.178 for the test set. In this study, 3D prediction models showed better predictive power than 2D models for the test set. This might be because 3D information is more important in the case of datasets containing compounds with similar skeletons. Superimposition of CoMFA contour maps in the active site of the PDF crystal structure showed a meaningful correlation between receptor–ligand binding and biological activity. The final QSAR models, along with information gathered from 3D contour and 2D contribution maps, could be useful for the design of novel active inhibitors of PDF. Figure Superimposition of comparative molecular field analysis (CoMFA) contour plot in the active site of peptide deformylase (PDF)     

A combined approach of docking and 3D QSAR study of beta-ketoacyl-acyl carrier protein synthase III (FabH) inhibitors

The enzyme FabH catalyzes the initial step of fatty acid biosynthesis via a type II fatty acid synthase. The pivotal role of this essential enzyme combined with its unique structural features and ubiquitous occurrence in bacteria has made it an attractive new target for the development of antibacterial and antiparasitic compounds. Three-dimensional quantitative structure-activity relationship (3D QSAR) studies such as comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) and docking simulations were conducted on a series of potent benzoylaminobenzoic acids. Docking studies were employed to position the inhibitors into the FabH active site to determine the probable binding conformation. A reasonable correlation between the predicated binding free energy and the inhibitory activity was found. CoMFA and CoMSIA were performed based on the docking conformations, giving q(2) of 0.637 and 0.697 for CoMFA and CoMSIA models, respectively. The predictive ability of the models was validated using a set of compounds that were not included in the training set and progressive scrambling test. Mapping the 3D QSAR models to the active site of FabH related that some important amino acid residues are responsible for protein-inhibitor interaction. These results should be applicable to the prediction of the activities of new FabH inhibitors, as well as providing structural understanding.     

CoMFA and CoMSIA 3D QSAR analysis on N1-arylsulfonylindole compounds as 5-HT6 antagonists

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) three-dimensional quantitative structure-activity relationship (3D-QSAR) studies were conducted on a series of N(1)-arylsulfonylindole compounds as 5-HT(6) antagonists. Evaluation of 20 compounds served to establish the models. The lowest energy conformer of compound 1 obtained from random search was used as template for alignment. The best predictions were obtained with CoMFA standard model (q2 = 0.643, r2 = 0.939 ) and with CoMSIA combined steric, electrostatic, hydrophobic, and hydrogen bond acceptor fields (q2 = 0.584, r2 = 0.902 ). Both the models were validated by an external test set of eight compounds giving satisfactory predictive r2 values of 0.604 and 0.654, respectively. The information obtained from CoMFA and CoMSIA 3D contour maps can be used for further design of specific 5-HT(6) antagonists.     

Molecular modeling study for the design of novel acetyl-CoA carboxylase inhibitors using 3D QSAR,molecular docking and dynamic simulations

Acetyl-CoA carboxylase (ACC) enzyme plays an important role in the regulation of biosynthesis and oxidation of fatty acids. ACC is a recognized drug target for the treatment of obesity and diabetes. Combination of ligand and structure-based in silico methods along with activity and toxicity prediction provides best lead compounds in the drug discovery process. In this study, a data-set of 100 ACC inhibitors were used for the development of comparative molecular field analysis (CoMFA) and comparative molecular similarity index matrix analysis (CoMSIA) models. The generated contour maps were used for the design of novel ACC inhibitors. CoMFA and CoMSIA models were used for the predication of activity of designed compounds. In silico toxicity risk prediction study was carried out for the designed compounds. Molecular docking and dynamic simulations studies were performed to know the binding mode of designed compounds with the ACC enzyme. The designed compounds showed interactions with key amino acid residues important for catalysis, and good correlation was observed between binding free energy and inhibition of ACC.     

Three-dimensional quantitative structure–activity relationship study on paullones as CDK inhibitors using CoMSIA and CoMFA

3D-QSAR studies were conducted on a series of paullones as CDK inhibitors using three-dimensional quantitative structure-activity relationship (3D-QSAR) methods. Two methods were compared: the widely used comparative molecular field analysis (CoMFA) and the recently reported comparative molecular similarity indices analysis (CoMSIA). Systematic variations of some parameters in CoMSIA and CoMFA were performed to search for the best 3D-QSAR model. The computed results showed that the 3D-QSAR models from CoMSIA were clearly superior to those from CoMFA. Using the best model from CoMSIA analysis, a significant cross-validated q² was obtained and the predicted biological activities of the five compounds in the test set were in good agreement with the experimental values. The correlation results obtained from CoMSIA were graphically interpreted in terms of field contribution maps allowing physicochemical properties relevant for binding to be easily mapped back onto molecular structures. The features in the CoMSIA contour maps intuitively suggested where to modify a molecular structure in terms of physicochemical properties and functional groups in order to improve its binding affinity, which is very important for improving our understanding of the ligand-receptor interactions and in helping to design compounds with improved activity.     

Pharmacophore-based 3D-QSAR as a predictive method for the QSAR analysis on a series of potent and selective inhibitors for three kinases of RTK family

For targets belonging to the same family of receptors, inhibitors often act at more than one biological target and produce a synergistic effect. Separate pharmacophore-based comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models were developed from our data-set for the kinase insert domain-containing receptor (KDR), cKit and Flt1 inhibitors. These models showed excellent internal predictability and consistency; validation using test-set compounds yielded a good predictive power for the pIC₅₀-value. The field contour maps (CoMFA and CoMSIA) corresponding to the KDR, cKit and Flt1 kinase subtypes reflected the characteristic similarities and differences between these types. These maps provided valuable information to facilitate structural modifications of the inhibitor to increase selectivity of the KDR over cKit and Flt1.     

Models of the pharmacophoric pattern and affinity trend of methyl 2-(aminomethyl)-1-phenylcyclopropane-1-carboxylate derivatives as σ1 ligands

Sigma-1 (σ₁) affinities of methyl 2-(aminomethyl)-1-phenylcyclopropane-1-carboxylate (MAPCC) derivatives were modelled by the genetic algorithm with linear assignment of hypermolecular alignment of datasets (GALAHAD) and the comparative molecular field analysis (CoMFA)/comparative molecular similarity indices analysis (CoMSIA) methods. GALAHAD was used for deriving the 3D pharmacophore pattern that encompasses the most potent σ₁ ligands within this series. Five MAPCC derivatives with a high σ₁ affinity were used for deriving this model. The obtained model included a nitrogen atom, the hydrophobes and the hydrogen bond acceptor features; it was able to identify other potent σ₁ ligands. On the other hand, CoMFA and CoMSIA methods were used for deriving quantitative structure–activity relationship (QSAR) models. All QSAR models were trained with 17 compounds, after which they were evaluated for predictive ability with additional five compounds. The best QSAR model was obtained by using CoMSIA, including steric, electrostatic and hydrophobic fields, and had a good predictive quality according to both internal and external validation criteria. In general, the models described herein provide meaningful information relevant for the rational design of new σ₁ ligands.     

3D-QSAR studies of farnesyltransferase inhibitors: a comparative molecular field analysis approach

3D-QSAR analysis has been performed on a series of previously synthesized benzonitrile derivatives, which were screened as farnesyltransferase inhibitors, using comparative molecular field analysis (CoMFA) with partial least-square fit to predict the steric and electrostatic molecular field interactions for the activity. The CoMFA study was carried out using a training set of 34 compounds. The predictive ability of the model developed was assessed using a test set of eight compounds (r(pred)(2) as high as 0.770). The analyzed 3D-QSAR CoMFA model has demonstrated a good fit, having r(2) value of 0.991 and cross-validated coefficient q(2) value as 0.619. The analysis of CoMFA contour maps provided insight into the possible modification of the molecules for better activity.     

Molecular docking and 3D QSAR studies on 1-amino-2-phenyl-4-(piperidin-1-yl)-butanes based on the structural modeling of human CCR5 receptor

In the present study, we have used an approach combining protein structure modeling, molecular dynamics (MD) simulation, automated docking, and 3D QSAR analyses to investigate the detailed interactions of CCR5 with their antagonists. Homology modeling and MD simulation were used to build the 3D model of CCR5 receptor based on the high-resolution X-ray structure of bovine rhodopsin. A series of 64 CCR5 antagonists, 1-amino-2-phenyl-4-(piperidin-1-yl)-butanes, were docked into the putative binding site of the 3D model of CCR5 using the docking method, and the probable interaction model between CCR5 and the antagonists were obtained. The predicted binding affinities of the antagonists to CCR5 correlate well with the antagonist activities, and the interaction model could be used to explain many mutagenesis results. All these indicate that the 3D model of antagonist-CCR5 interaction is reliable. Based on the binding conformations and their alignment inside the binding pocket of CCR5, three-dimensional structure-activity relationship (3D QSAR) analyses were performed on these antagonists using comparative molecular field analysis (CoMFA) and comparative molecular similarity analysis (CoMSIA) methods. Both CoMFA and CoMSIA provide statistically valid models with good correlation and predictive power. The q(2)(r(cross)(2)) values are 0.568 and 0.587 for CoMFA and CoMSIA, respectively. The predictive ability of these models was validated by six compounds that were not included in the training set. Mapping these models back to the topology of the active site of CCR5 leads to a better understanding of antagonist-CCR5 interaction. These results suggest that the 3D model of CCR5 can be used in structure-based drug design and the 3D QSAR models provide clear guidelines and accurate activity predictions for novel antagonist design.