Molecular modeling of some 1H-benzimidazole derivatives with biological activity against Entamoeba histolytica: a comparative molecular field analysis study

Comparative molecular field analysis (CoMFA) was performed on a set of 1H-benzimidazole derivatives. Molecular modeling and 3D-QSAR were employed to determine the tautomeric form that would probably fit a target receptor in Entamoeba histolytica. CoMFA results suggest that the antiamoebic activity is favored with steric bulk at position 5 of the benzimidazole ring and low electron density on the group at position 2. To the best of our knowledge this is the first 3D-QSAR study performed for benzimidazoles as antiamoebic agents. The CoMFA models derived will be very valuable to design new and more potent compounds against E. histolytica.     

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.     

CoMFA and CoMSIA 3D-QSAR analysis of DMDP derivatives as anti-cancer agents

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) based on three dimensional quantitative structure-activity relationship (3D-QSAR) studies were conducted on a series (78 compounds) of 2, 4-diamino-5-methyl-5-deazapteridine (DMDP) derivatives as potent anticancer agents. The best prediction were obtained with a CoMFA standard model (q(2) = 0.530, r(2) = 0.903) and with CoMSIA combined steric, electrostatic, hydrophobic and hydrogen bond donor fields (q(2) = 0.548, r(2) = 0.909). Both models were validated by a test set of ten compounds producing very good predictive r(2) values of 0.935 and 0.842, respectively. CoMFA and CoMSIA contour maps were then used to analyze the structural features of ligands to account for the activity in terms of positively contributing physiochemical properties such as steric, electrostatic, hydrophobic and hydrogen bond donor fields. The resulting contour maps produced by the best CoMFA and CoMSIA models were used to identify the structural features relevant to the biological activity in this series of analogs. This study suggests that the highly electropositive substituents with low steric tolerance are required at 5 position of the pteridine ring and bulky electronegatve substituents are required at the meta-position of the phenyl ring. The information obtained from CoMFA and CoMSIA 3-D contour maps can be used for the design of deazapteridine-based analogs as anticancer agents.     

CoMFA and CoMSIA 3D-QSAR analysis of diaryloxy-methano-phenanthrene derivatives as anti-tubercular agents

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) based on three-dimensional quantitative structure-activity relationship (3D-QSAR) studies were conducted on a series (44 compounds) of diaryloxy-methano-phenanthrene derivatives as potent antitubercular agents. The best predictions were obtained with a CoMFA standard model (q (2)=0.625, r (2)=0.994) and with CoMSIA combined steric, electrostatic, and hydrophobic fields (q (2)=0.486, r (2)=0.986). Both models were validated by a test set of seven compounds and gave satisfactory predictive r (2) values of 0.999 and 0.745, respectively. CoMFA and CoMSIA contour maps were used to analyze the structural features of the ligands to account for the activity in terms of positively contributing physicochemical properties: steric, electrostatic, and hydrophobic fields. The information obtained from CoMFA and CoMSIA 3-D contour maps can be used for further design of phenanthrene-based analogs as anti-TB agents. The resulting contour maps, produced by the best CoMFA and CoMSIA models, were used to identify the structural features relevant to the biological activity in this series of analogs. Further analysis of these interaction-field contour maps also showed a high level of internal consistency. This study suggests that introduction of bulky and highly electronegative groups on the basic amino side chain along with decreasing steric bulk and electronegativity on the phenanthrene ring might be suitable for designing better antitubercular agents.     

Three-dimensional quantitative structure–farnesyltransferase inhibition analysis for some diaminobenzophenones

A 3D-QSAR investigation of 95 diaminobenzophenone yeast farnesyltransferase (FT) inhibitors selected from the work of Schlitzer et al. showed that steric, electrostatic, and hydrophobic properties play key roles in the bioactivity of the series. A CoMFA/CoMSIA combined model using the steric and electrostatic fields of CoMFA together with the hydrophobic field of CoMSIA showed significant improvement in prediction compared with the CoMFA steric and electrostatic fields model. The similarity of the 3D-QSAR field maps for yeast FT inhibition activity (from this work) and for antimalarial activity data (from previous work) and the correlation between those activities are discussed.     

3D-QSAR CoMFA/CoMSIA studies on Urokinase plasminogen activator (uPA) inhibitors: a strategic design in novel anticancer agents

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) was performed on a series of indole/benzoimidazole-5-carboxamidines as urokinase plasminogen activator (uPA) inhibitors. The ligand molecular superimposition on template structure was performed by atom/shape-based RMS fit, multifit, and RMSD fit methods. The removal of two outliers from the initial training set of 30 molecules improved the predictivity of the models. The statistically significant model was established from 28 molecules, which were validated by evaluation of test set of nine compounds. The atom-based RMS alignment yielded best predictive CoMFA model (r2(cv) = 0.611, r2(cnv) = 0.778, F value = 43.825, r2(bs) = 0.842, r2(pred) = 0.616 with two components) while the CoMSIA model yielded (r2(cv) = 0.499, r2(cnv) = 0.976, F value=96.36, r2(bs) = 0.993, r2(pred) = 0.694 with eight components). The contour maps obtained from 3D-QSAR studies were appraised for the activity trends of the molecules analyzed. The results indicate that the steric, electrostatic, and hydrogen bond donor/acceptor substituents play significant role in uPA activity and selectivity of these compounds. The data generated from the present study can be used as putative pharmacophore in the design of novel, potent, and selective urokinase plasminogen activator inhibitors as cancer therapeutics.     

3D-QSAR CoMFA studies on bis-coumarine analogues as urease inhibitors: a strategic design in anti-urease agents

A 3D-QSAR study has been performed on thirty (30) bis-coumarine derivatives to correlate their chemical structures with their observed urease inhibitory activity. Due to the absence of information on their active mechanism, comparative molecular field analysis (CoMFA) was used in the study. Two different properties: steric, electrostatic, assumed to cover the major contributions to ligand binding, were used to generate the 3D-QSAR model. Significant cross-validated correlation coefficients q(2) (0.558) and r(2) (0.992) for CoMFA were obtained, indicating the statistical significance of this class of compounds. The red electrostatic contour map highlighting those portion of compounds which may be interacting with nickel metal center in the active site of urease; while the blue contour map indicates positively charged groups in the ligands have improved biological activity and thus lower the IC(50)s. The steric contour map shows that bulkier substitutions at the 'R' position are detrimental to ligand receptor interaction. Actual urease inhibitory activities of this class and the predicted values were in good agreement with the experimental results. Moreover, from the contour maps, the key features vital to ligand binding have been identified, which are important for us to trace the important properties and gain insight into the potential mechanisms of intermolecular interactions between the ligand and receptor.     

CoMFA and CoMSIA 3D-QSAR analysis on hydroxamic acid derivatives as urease inhibitors

Urease (EC 3.5.1.5) serves as a virulence factor in pathogens that are responsible for the development of many diseases in humans and animals. Urease allows soil microorganisms to use urea as a source of nitrogen and aid in the rapid break down of urea-based fertilizers resulting in phytopathicity. It has been well established that hydroxamic acids are the potent inhibitors of urease activity. The 3D-QSAR studies on thirty five hydroxamic acid derivatives as known urease inhibitors were performed by Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) methods to determine the factors required for the activity of these compounds. The CoMFA model produced statistically significant results with cross-validated (q(2)) 0.532 and conventional (r(2)) correlation coefficients 0.969.The model indicated that the steric field (70.0%) has greater influence on hydroxamic acid inhibitors than the electrostatic field (30.0%). Furthermore, five different fields: steric, electrostatic, hydrophobic, H-bond donor and H-bond acceptor assumed to generate the CoMSIA model, which gave q(2) 0.665 and r(2) 0.976.This model showed that steric (43.0%), electrostatic (26.4%) and hydrophobic (20.3%) properties played a major role in urease inhibition. The analysis of CoMFA and CoMSIA contour maps provided insight into the possible modification of the hydroxamic acid derivatives for improved activity.     

Comparative molecular field analysis and comparative molecular similarity indices analysis of boron-containing human thymidine kinase 1 substrates

Three-dimensional quantitative structure-activity relationship (3D-QSAR) using CoMFA and CoMSIA techniques was applied to evaluate 56 pyrimidine nucleosides as substrates of human thymidine kinase 1 (hTK1), 27 of them containing a carborane substituent either at the 3-, 5-, or 3'-position of the 2'-deoxyuridine scaffold. This is the first report describing 3D-QSAR studies of compounds containing boron atoms. Both CoMFA and CoMSIA models were derived from a training set of 47 molecules and the predictive capacity of the CoMSIA model was successfully validated by accurately calculating known phosphorylation rates of both boronated and non-boron hTK1 substrates that were not included in the training set. The optimal CoMSIA model provided the following values: q(2) 0.622, r(2) 0.983, s 0.165, and F 187.5. Contour maps obtained from the CoMSIA model were in agreement with the experimentally determined biological data.     

3D-QSAR Study on dihydro-1,3,5-triazines and their spiro derivatives as DHFR inhibitors by comparative molecular field analysis (CoMFA)

A 3D-QSAR/CoMFA was performed for a series of triazine and its spiro derivative based DHFR inhibitors displaying IC(50) values ranging from 0.002 to 58.8 μM. Analyses resulted in a reliable computational model with the parameters of n=46, r(2)=0.986, q(2)=0.724, SE=0.164, F=275.889. It is shown that the steric and electrostatic properties predicted by CoMFA contours can be related to the DHFR inhibitory activity. The predictive ability of the resultant model was evaluated using a test set comprised of 18 molecules and the results show that the CoMFA model is able to correctly predict the poor inhibitory activities of the compounds in the testing set. This model is a significant guide to trace the features that really matter especially with respect to the design of novel compounds.     

3D-QSAR study of adamantyl N-benzylbenzamides as melanogenesis inhibitors

Three-dimensional quantitative structure–activity relationship (3D-QSAR) modeling, comparative molecular field analysis (CoMFA), and comparative molecular similarity indices analysis (CoMSIA) of polyhydroxylated N-benzylbenzamide derivatives containing an adamantyl moiety were performed to understand the mechanism of action and structure–activity relationship of these compounds. Contour map analysis indicated that steric contributions of the adamantyl moiety and electrostatic contributions of the hydroxyl group at the 3-position are important in the activity. Activities of the training set and test sets predicted by CoMFA fit well with actual activities, demonstrating that CoMFA, along with the best calculated q² value, has the best predictive ability.     

3D-QSAR analysis of antimalarial farnesyltransferase inhibitors based on a 2,5-diaminobenzophenone scaffold

With annual death tolls in the millions and emerging resistance to existing drugs, novel therapies are needed against malaria. Wiesner et al. recently developed a novel class of antimalarials derived from farnesyltransferase inhibitors based on a 2,5-diaminobenzophenone scaffold. The compounds displayed a wide range of activity, including submicromolar, against the multi-drug resistant Plasmodium falciparum strain Dd2. In order to investigate quantitatively the local physicochemical properties involved in the interaction between drug and biotarget, we used the 3D-QSAR methods CoMFA and CoMSIA to study some of the series, including the screened lead compound 2,5-bis-acylaminobenzophenone, 28 cinnamic acid derivatives, 29 N-(3-benzoyl-4-tolylacetylaminophenyl)-3-(5-aryl-2-furyl)acrylic acid amides, and 34 N-(4-substituted-amino-3-benzoylphenyl)-[5-(4-nitrophenyl)-2-furyl]acrylic acid amides. We found that steric, electrostatic, and hydrophobic properties of substituent groups play key roles in the bioactivity of the series of compounds, while hydrogen bonding interactions show no obvious impact. We built several highly predictive 3D-QSAR models, including a CoMSIA one composed of steric, electrostatic, and hydrophobic fields, with r(2)=0.94, q(2)=0.63, and r(pred)(2)=0.63. The results provide insight for optimization of this class of antimalarials for better activity and may prove helpful for further lead optimization.     

Quantitative structure-activity relationship (QSAR) of indoloacetamides as inhibitors of human isoprenylcysteine carboxyl methyltransferase

A QSAR is developed for the isoprenylcysteine carboxyl methyltransferase (ICMT) inhibitory activities of a series of indoloacetamides (n=72) that are structurally related to cysmethynil, a selective ICMT inhibitor. Multivariate analytical tools (principal component analysis (PCA) and projection to latent structures (PLS)), multi-linear regression (MLR) and comparative molecular field analysis (CoMFA) are used to develop a suitably predictive model for the purpose of optimizing and identifying members with more potent inhibitory activity. The resulting model shows that good activity is determined largely by the characteristics of the substituent attached to the indole nitrogen, which should be a lipophilic residue with fairly wide dimensions. In contrast, the substituted phenyl ring attached to the indole ring must be of limited dimensions and lipophilicity.     

3D-QSAR analysis of a new type of acetylcholinesterase inhibitors

Acetylcholinesterase (AChE) inhibitors are an important class of medicinal agents used for the treat- ment of Alzheimer's disease. A screening model of AChE inhibitor was used to evaluate the inhibition of a series of phenyl pentenone derivatives. The assay result showed that some compounds displayed higher inhibitory effects. In order to study the relationship between the bioactivities and the structures, 26 compounds with phenyl pentenone scaffold were analyzed. A 3D-QSAR model was constructed us- ing the method of comparative molecular field analysis (CoMFA). The results of cross-validated R2cv=0.629, non-cross-validated R2=0.972, SE=0.331, and F=72.41 indicate that the 3D-model pos- sesses an ability to predict the activities of new inhibitors, and the CoMFA model would be useful for the future design of new AChE inhibitors.     

Three-dimensional structure-activity relationship modeling of cocaine binding to two monoclonal antibodies by comparative molecular field analysis

Successful immunotherapy of cocaine addiction and overdoses requires cocaine-binding antibodies with specific properties, such as high affinity and selectivity for cocaine. We have determined the affinities of two cocaine-binding murine monoclonal antibodies (mAb: clones 3P1A6 and MM0240PA) for cocaine and its metabolites by [3H]-radioligand binding assays. mAb 3P1A6 (K(d) = 0.22 nM) displayed a 50-fold higher affinity for cocaine than mAb MM0240PA (K(d) = 11 nM) and also had a greater specificity for cocaine. For the systematic exploration of both antibodies' binding specificities, we used a set of approximately 35 cocaine analogues as structural probes by determining their relative binding affinities (RBAs) using an enzyme-linked immunosorbent competition assay. Three-dimensional quantitative structure-activity relationship (3D-QSAR) models on the basis of comparative molecular field analysis (CoMFA) techniques correlated the binding data with structural features of the ligands. The analysis indicated that despite the mAbs' differing specificities for cocaine, the relative contributions of the steric (approximately 80%) and electrostatic (approximately 20%) field interactions to ligand-binding were similar. Generated three-dimensional CoMFA contour plots then located the specific regions about cocaine where the ligand/receptor interactions occurred. While the overall binding patterns of the two mAbs had many features in common, distinct differences were observed about the phenyl ring and the methylester group of cocaine. Furthermore, using previously published data, a 3D-QSAR model was developed for cocaine binding to the dopamine reuptake transporter (DAT) that was compared to the mAb models. Although the relative steric and electrostatic field contributions were similar to those of the mAbs, the DAT cocaine-binding site showed a preference for negatively charged ligands. Besides establishing molecular level insight into the interactions that govern cocaine binding specificity by biopolymers, the three-dimensional images obtained reflect the properties of the mAbs binding pockets and provide the initial information needed for the possible design of novel antibodies with properties optimized for immunotherapy.     

Influence of the separation of the charged groups and aromatic ring on interaction of tyrosine and phenylalanine analogues and derivatives with beta-cyclodextrin

Interactions of tyrosine and phenylalanine analogues with beta-cyclodextrin have been examined in terms of structural features of the ligand such as the separation of the charged amino group and aromatic ring, the presence of additional functional group attached to the amino or phenyl ring, and the presence of a charge on amino or carboxyl group, and steric effects using steady-state and time-resolved fluorescence spectroscopy and microcalorimetry. The studied aromatic amino acids possess low binding constant to beta-cyclodextrin, diversified with respect to the presence or absence of a substituent in para position of the phenyl ring. However, calculated, based on the global analysis of the fluorescence intensity decays, binding constants do not allow to estimate unequivocally the influence of the distance between the charged groups and phenol/phenyl ring on the inclusion complex stability because of their low diversification.     

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.     

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.     

3D-QSAR analysis of a new type of acetylcholinesterase inhibitors

Acetylcholinesterase (AChE) inhibitors are an important class of medicinal agents used for the treatment of Alzheimer’s disease. A screening model of AChE inhibitor was used to evaluate the inhibition of a series of phenyl pentenone derivatives. The assay result showed that some compounds displayed higher inhibitory effects. In order to study the relationship between the bioactivities and the structures, 26 compounds with phenyl pentenone scaffold were analyzed. A 3D-QSAR model was constructed using the method of comparative molecular field analysis (CoMFA). The results of cross-validated R²cv=0.629, non-cross-validated R²=0.972, SE=0.331, and F=72.41 indicate that the 3D-model possesses an ability to predict the activities of new inhibitors, and the CoMFA model would be useful for the future design of new AChE inhibitors.