3D-QSAR analysis on benzazole derivatives as eukaryotic topoisomerase II inhibitors by using comparative molecular field analysis method

Selective topoisomerase II inhibitors have created a great deal of interest in recent years for the design of new antitumoral compounds. 3D-QSAR analysis has been performed on a series of previously synthesized benzoxazole, benzimidazole, and oxazolo(4,5-b)pyridine derivatives, which are screened as eukaryotic topoisomerase II inhibitors, using comparative molecular field analysis (CoMFA) with partial least squares fit to predict the steric and electrostatic molecular field interactions for the activity. The CoMFA study was carried out using a training set of 16 compounds. The predictive ability of the model was assessed using a test set of 7 compounds. The analyzed 3D-QSAR CoMFA model has demonstrated a good fit, having r(2) value of 0.997 and cross-validated coefficient q(2) value as 0.435 for the model. The obtained model reveals that the electronegatively charged substituents such as NO(2) or COOCH(3) group on position R and/or R(1) at the heterocyclic ring system and positively charged atom and/or atom groups located between the benzazole moiety and 2-substituted phenyl ring as a bridge element improve the activity. On the other hand, a bulky substituent, such as methoxy group, attached to the ortho position of 2-phenyl-5-nitro-benzoxazole (1) enhances the activity similar to compound 13, which is both a meta and para substituent of the phenyl group attached to the 2-position of benzimidazole ring system, fit into the favored steric region to improve the activity.     

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.     

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.     

3D-QSAR of N-myristoyltransferase inhibiting antifungal agents by CoMFA and CoMSIA methods

A series of benzofuran antifungals was examined to determine the structural requirements of N-myristoyltransferase (Nmt) enzyme inhibition by three-dimensional quantitative structure-activity relationship (3D-QSAR) using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods. Evaluation of 20 compounds (training set) served to establish the model, which was validated by evaluation of a set of 6 compounds (test set). The lowest energy conformer of the most active molecule obtained from systematic search was used as the template structure for the alignment. The best predictions were obtained with the CoMFA model from RMS fit, with r(2)(cv)=0.828, r(2)(conv)=0.989, r(2)(pred)=0.754 and with the CoMSIA model combining hydrophobic, hydrogen bond donor and hydrogen bond acceptor fields with r(2)(cv)=0.821, r(2)(conv)=0.978 and r(2)(pred)=0.747. The models obtained from the present study can be useful for the development of new Nmt inhibitors as potential antifungals. The docking studies were also carried out wherein the active and inactive molecules were docked into the active site of the recently reported Candida albicans Nmt (CaNmt) crystal structure to analyze enzyme-inhibitor interactions. The results obtained from the present 3D-QSAR and docking studies were found complimentary.     

Molecular docking and 3D-QSAR studies on inhibitors of DNA damage signaling enzyme human PARP-1

Poly (ADP-ribose) polymerase-1 (PARP-1) operates in a DNA damage signaling network. Molecular docking and three dimensional-quantitative structure activity relationship (3D-QSAR) studies were performed on human PARP-1 inhibitors. Docked conformation obtained for each molecule was used as such for 3D-QSAR analysis. Molecules were divided into a training set and a test set randomly in four different ways, partial least square analysis was performed to obtain QSAR models using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). Derived models showed good statistical reliability that is evident from their r2, q2(loo) and r2(pred) values. To obtain a consensus for predictive ability from all the models, average regression coefficient r2(avg) was calculated. CoMFA and CoMSIA models showed a value of 0.930 and 0.936, respectively. Information obtained from the best 3D-QSAR model was applied for optimization of lead molecule and design of novel potential inhibitors.     

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 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.     

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.     

3D-QSAR and molecular docking studies of 2-pyrimidinecarbonitrile derivatives as inhibitors against falcipain-3

The three dimensional-quantitative structure activity relationship (3D-QSAR) studies were performed on a series of falcipain-3 inhibitors using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) techniques. A training set containing 42 molecules served to establish the QSAR models. The optimum CoMFA and CoMSIA models obtained for the training set were statistically significant with cross-validated correlation coefficients r(cv)(2) (q(2)) of 0.549 and 0.608, and conventional correlation coefficients (r(2)) of 0.976 and 0.932, respectively. An independent test set of 12 molecules validated the external predictive power of both models with predicted correlation coefficients (r(pred)(2)) for CoMFA and CoMSIA as 0.697 and 0.509, respectively. The docking of inhibitors into falcipain-3 active site using GOLD software revealed the vital interactions and binding conformation of the inhibitors. The CoMFA and CoMSIA field contour maps agree well with the structural characteristics of the binding pocket of falcipain-3 active site, which suggests that the information rendered by 3D-QSAR models and the docking interactions can provide guidelines for the development of improved falcipain-3 inhibitors.     

3D-QSAR design of novel antiepileptic sulfamides

A three-dimensional quantitative structure-activity relationship method, the comparative molecular field analysis (CoMFA), was applied to design new anticonvulsant symmetric sulfamides. The training set (27 structures) was comprised by traditional and new-generation anticonvulsant (AC) ligands that exhibit a potent activity in MES test. Physicochemical determinants of binding, such as steric and electrostatic properties, were mapped onto the molecular structures of the set, in order to interpret graphically the CoMFA results in terms of field contribution maps. The 3D-QSAR models demonstrate a good ability to predict the activity of the designed compounds (r(2)=0.967, q(2)=0.756).     

New potent 5-substituted benzofuroxans as inhibitors of Trypanosoma cruzi growth: quantitative structure-activity relationship studies

Benzofuroxan derivatives have been shown to inhibit the growth of Trypanosoma cruzi, the etiological agent of Chagas' disease. Therefore, 2D- and 3D-QSAR models of their in vitro antichagasic activity were developed. Six new derivatives were synthesized to complete a final set of 26 structurally diverse benzofuroxans. The 2D-QSAR model (r = 0.939, r(adj)(2) = 0.849) was generated using multiple regression analysis of tabulated substituents' physicochemical properties and indicator variables. In addition, a 3D-QSAR model (r(2) = 0.997, q(2) = 0.802) was obtained using a comparative molecular field analysis (CoMFA). Due to the well-known benzofuroxan tautomerism, in both approaches (2D- and 3D-QSAR) it was necessary to include an indicator variable to consider the N-oxide position (I(6)). This parameter was established using low-temperature NMR experiments. Both QSAR models identified the electrophilic character of the substituent alpha-atom as a requirement for activity. Further support was found using a density functional theory (DFT) approach.     

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 studies on T-type calcium channel blockers using CoMFA and CoMSIA

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on a series of isoxazolyl compounds as a potent T-type calcium channel blockers. A set of 24 structurally similar compounds served to establish the model. Four different conformations of the most active compound were used as template structures for the alignment, three of which were obtained from Catalyst pharmacophore modeling and one by using SYBYL random search option. All CoMFA and CoMSIA models gave cross-validated r(2) (q(2)) value of more than 0.5 and conventional r(2) value of more than 0.85. The predictive ability of the models was validated by an external test set of 10 compounds, which gave satisfactory pred r(2) values ranging from 0.577 to 0.866 for all models. Best predictions were obtained with CoMFA std model of Conformer no: 3 alignment (q(2)=0.756, r(2)=0.963), giving predictive r(2) value of 0.866 for the test set. CoMFA and CoMSIA contour maps were used to analyze the structural features of the ligands accounting for the activity in terms of positively contributing physicochemical properties: steric, electrostatic, hydrophobic and hydrogen bonding fields.     

黄酮类醛糖还原酶抑制剂的三维定量构效关系研究

目的：建立黄酮类化合物抑制剂活性的三维定量构效关系模型,为进一步进行黄酮类醛糖还原酶抑制剂（ARI）的活性与三维结构关系的研究提供重要依据。方法：采用比较分子力场分析法（CoMFA）和比较分子相似性指数分析法（CoMSIA）,系统研究了75个新型ARI的三维定量构效关系。结果：CoMFA和CoMSIA模型的交互验证相关系数q^2值分别为0.603和0．706、非交互验证相关系数r2值分别为0.956和0.900。结论：CoMFA和CoMSIA模型均具有较强的预测能力,CoMFA和CoMSIA模型的三维等值线图直观地解释了化合物的构效关系,阐明了化合物结构中各位置取代基对黄酮类醛糖还原酶抑制剂活性的影响,为进一步结构优化提供了重要理论依据。     

3D-QSAR study of ring-substituted quinoline class of anti-tuberculosis agents

A 3D-QSAR analysis of a new class of ring-substituted quinolines with anti-tuberculosis activity has been carried out by three methods-Comparative Molecular Field Analysis (CoMFA), CoMFA with inclusion of a hydropathy field (HINT), and Comparative Molecular Similarity Indices Analysis (CoMSIA). The conformation of the molecules was generated using a simulated annealing protocol and they were superimposed using features common to the set with database alignment (SYBYL) and field fit methods. Several statistically significant CoMFA, CoMFA with HINT, and CoMSIA models were generated. Prediction of the activity of a set of test molecules was the best for the CoMFA model generated with database alignment. Based upon the information contained in the CoMFA model, we have identified some novel features that can be incorporated into the quinoline framework to improve the activity.     

Three-dimensional quantitative structure activity relationships (3-D-QSAR) of antihyperglycemic agents.

A three-dimensional quantitative structure activity relationship study (3-D-QSAR) was performed on a set of thiazolidinedione antihyperglycemic agents using the comparative molecular field analysis (CoMFA) method. The CoMFA models were derived from a training set of 53 compounds. Fifteen compounds, which were not used in model generation were used to validate the CoMFA models. All the compounds were superimposed to the template structure by atom-based and shape-based strategies. The SYBYL QSAR rigid body field fit was also used for aligning the ligands. A total of twelve different alignments were generated. The resulting models exhibited a good cross-validated r2cv values (0.624-0.764) and the conventional r2 values (0.689-0.921). A more robust cross-validation test using cross-validation by 2 groups (leave half out method) was performed 100 times to ascertain the predictiveness of the CoMFA models. The mean of r2cv values from 100 runs ranged from 0.611-0.690. Few models exhibited good external predictivity. These models were then used to define a hypothetical receptor model for antihyperglycemic agents.     

Three-dimensional quantitative structure-activity relationship (3D-QSAR) studies of tricyclic oxazolidinones as antibacterial agents

Oxazolidinones exemplified by eprezolid and linezolid are a new class of antibacterials that are active against Gram positive and anaerobic bacteria including methicillin-resistant Staphylococcus aureus (MRSA), methicillin-resistant Staphylococcus epidermidis (MRSE) and vancomycin resistant enterococci (VRE). In an effort to have a better antibacterial agent in the oxazolidinone class, we have performed three-dimensional quantitative structure-activity relationship (3D-QSAR) studies for a series of tricyclic oxazolidinones. 3D-QSAR studies were performed using the Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) procedures. These studies were performed using 42 compounds; the QSAR model was developed using a training set of 33 compounds. The predictive ability of the QSAR model was assessed using a test set of 9 compounds. The predictive 3D-QSAR models have conventional r(2) values of 0.975 and 0.940 for CoMFA and CoMSIA respectively; similarly, cross-validated coefficient q(2) values of 0.523 and 0.557 for CoMFA and CoMSIA, respectively, were obtained. The CoMFA 3D-QSAR model performed better than the CoMSIA model.     

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.     

The 3D-QSAR study of antitumor arylsulfonylimidazolidinone derivatives by CoMFA and CoMSIA

Three-dimensional quantitative structure-activity relationship (3D-QSAR) studies for a series of arylsulfonylimidazolidinone derivatives having antitumor activity were conducted using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The in vitro cytotoxicity against human lung carcinoma (A549) exhibited a strong correlation with steric and electrostatic factors of the molecules. Four different types of models have been built using CoMFA and CoMSIA method with AM1 charge or Gasteiger-Huckel charge. By comparison of the statistical results of these models, model I obtained by CoMFA study with AM1 showed the best predictability of the antitumor activities based on the cross-validated value (0.642), conventional r2 (0.981), standard error of estimate (0.106) and PRESS value (0.170).