Cross-validated Q2 Research Articles

A structure-activity relationship study of imidazole-5-carboxylic acids derivatives as angiotensin II receptor antagonists combining 2D and 3D QSAR methods.

Two Dimensional (2D) and Three Dimensional (3D) Quantitative Structure-Activity Relationship (QSAR) studies were performed for correlating the chemical composition of Imidazole-5-carboxylic Acids analogues and their Angiotensin II AT1 Receptor Antagonists activity using partial least squares and k Nearest Neighbor respectively. For Comparing the three different feature selection methods of 2D-QSAR, k Nearest Neighbor models was used in conjunction with simulated annealing (SA), genetic algorithm (GA) and stepwise (SW) coupled with Partial least square (PLS) showed variation in biological activity. The statistically significant best 2D-QSAR model having good predictive ability with statistical values of r2 = 0.8040, and pred_r2 = 0.7764, was developed by SA-Partial least square with the descriptors like SsCH3Count, 5Chain Count, SdsCHE-index and H-acceptor count showed that increase in the values of these descriptors are beneficial for the activity. The 3D-QSAR studies were performed using the SA-PLS a leave-one-out cross-validated correlation coefficient q2=0.7188 and predicate activity pred_r2 =0.7226 were obtained. The information rendered by QSAR models may lead to a better understanding of structural requirements of substituted Imidazole-5-carboxylic Acids derivatives and also aid in designing novel potent antihypertensive molecules.

Prospective QSAR-based prediction models with pharmacophore studies of oxadiazole-substituted α-isopropoxy phenylpropanoic acids on with dual activators of PPARα and PPARγ

A series of oxadiazole-substituted α-isopropoxy phenylpropanoic acids with dual activators of PPARα and PPARγ derivatives were subjected to two dimensional and k-nearest neighbour Molecular field analysis. The statistically significant best 2D-QSAR (PPARα) model having good predictive ability with statistical values of r2 = 0.8725, q2 = 0.7957 and pred_r2 = 0.8136, was developed by GA-PLS with the descriptors like SsClcount, SddsN (nitro) count and SsOHcount contribute significantly to the biological activity. The best 3D-QSAR studies (PPARα) were performed using the genetic algorithm selection k-nearest neighbor molecular field analysis approach; a leave-one-out cross-validated correlation coefficient q2=0.7188 and predicate activity pred_r2 =0.7508 were obtained. The influences of steric and electrostatic field effects generated by the contribution plots are discussed. The best pharmacophore model includes three features viz. hydrogen bond donor, hydrogen bond acceptor, and aromatic features were developed. The information rendered by 2D, 3D QSAR models may lead to a better understanding of structural requirements of substituted α-isopropoxy phenylpropanoic derivatives and also aid in designing novel potent PPARα and PPARγ for antihyperglycemic molecules.

Molecular modeling studies on substituted aminopyrimidines derivatives as potential antimalarial compounds

We report combined study of k-nearest neighbor, pharmacophore and 2D QSAR was performed on a series of 2,4-diaminopyrimidines dihydrofolate reductase from pyrimethamine-resistant Plasmodium falciparum as antimalarial agents to gain insights into the structural determinants and their structure–activity relationship. The QSAR models for the prediction of activity of antiplasmodial activities against P. falciparum clones with wild-type TM4/8.2 and K1CB1 strains have been developed by the SA-PLS and SW-PLS methods, and the proposed models gain satisfactory results. The statistically significant best 2D QSAR model having correlation coefficient r2 = 0.8569, 0.7853 and cross-validated squared correlation coefficient q2 = 0.7104, 0.7216 with external predictive ability of pred_r2 = 0.7995, 0.7064 was developed by wild-type TM4/8.2 and K1CB1 strains with SA-PLS. 3D QSAR studies using k-nearest neighbor molecular field analysis (kNN-MFA) method, identifies two models obtained by SA-PLS and SW-PLS methods leading to antimalarial activity prediction. The obtained pharmacophore model with lowest RMSD value (0.1548 A), consisting of one hydrogen donor, two hydrogen acceptors and one aromatic region was developed. These models were found to yield reliable clues for further optimization of 2,4-diaminopyrimidines derivatives in the data set. We hope that these results will give new insights into chemical modifications that can be realized with the aim of designing new inhibitors with improved pharmacological properties.

3D QSAR modeling of 4-nerolidylcatechol derivatives and virtual screening for identification of potent <i>plasmodium</i> inhibitor

The present study was aim to develop a three dimensional quantitative structure-activity relationships (3D QSAR) model based on the structure of 4-nerolidylcatechol (IC50=0.67 µM), a novel plant derived Plasmodium inhibitor and its derivatives for identification of efficient antimalarial lead. A statisti-cally validated Partial Least-Squares (PLS) based Molecular Field Analysis (MFA) model was built up using the training set of eight 4-nerolidylcatechol derivatives and their diverse conformers. A statistically reliable model with good predictive power (cross-validated correlation coefficient q2=0.769) was obtained. Hence, the generated model was used to screen a library of 30,000 compounds of chembridge database (http://www.chembridge.com). Results of drug likeness prediction and ADMET study has suggested six compounds as potential antimalarial/plasmodial lead.

Drug discovery studies on quinoline-based derivatives as potential antimalarial agents

Molecular modelling studies were performed to identify the essential structural requirements of quinoline-based derivatives for improving their antimalarial activity. The developed CoMFA, CoMSIA and HQSAR models for a training set comprising 37 derivatives showed good statistical significance in terms of internal cross validation (q2) 0.70, 0.69 and 0.80 and non-cross validation (r2) 0.80, 0.79 and 0.80. Also, the predicted r2 values (r2pred) of 0.63, 0.61 and 0.72 for a test set consisting of 12 compounds suggested significant predicting ability of the models. Structural features were correlated in terms of steric, electrostatic, hydrophobic, hydrogen bond donor and hydrogen bond acceptor interactions. Furthermore, the bioactive conformation was explored and explained by docking compounds #28, 32 and 40 into the active binding site of lactate dehydrogenase of Plasmodium falciparum. The QSAR models, contour map and docking binding affinity obtained could be successfully utilized as a guiding tool for the design and discovery of novel quinoline-based derivatives with potent antimalarial activity.

3D-QSAR study using CoMFA and CoMSIA methods for a series of histone H3 phosphorylation inhibitors

Three-dimensional quantitative structure–activity relationship (3D-QSAR) analysis of inhibitory activities for a series of pyrrolotriazine derivatives against histone H3 phosphorylation (pHH3) was performed using comparative of molecular field analysis (CoMFA) and comparative of molecular similarities indices analysis (CoMSIA) techniques. 62 derivatives were used to establish and validate two models by considering a high deviation in biological activities and structural variations. Optimum CoMFA and CoMSIA models obtained from the training set were statistically significant with cross-validated correlation coefficients q 2 of 0.551 and 0.621, and conventional correlation coefficients (r 2) of 0.999 and 0.995, respectively. The predicted correlation coefficients of test set (R 2) for CoMFA and CoMSIA were 0.835 and 0.918, respectively. Two models obtained provide guidelines to trace the features that really matter chiefly with respect to the design of novel pyrrolotriazine derivatives.

Three-dimensional quantitative structure–activity relationship study on antioxidant capacity of curcumin analogues

A comparative molecular similarity indices analysis (CoMSIA) was performed on a set of 27 curcumin-like diarylpentanoid analogues with the radical scavenging activities. A significant cross-validated correlation coefficient Q2 (0.784), SEP (0.042) for CoMSIA were obtained, indicating the statistical significance of the correlation. Further we adopt a rational approach toward the selection of substituents at various positions in our scaffold,and finally find the favored and disfavoured regions for the enhanced antioxidative activity. The results have been used as a guide to design compounds that, potentially, have better activity against oxidative damage.

Quantitative structure-activation barrier relationship modeling for Diels-Alder ligations utilizing quantum chemical structural descriptors

BackgroundIn the present study, we show the correlation of quantum chemical structural descriptors with the activation barriers of the Diels-Alder ligations. A set of 72 non-catalysed Diels-Alder reactions were subjected to quantitative structure-activation barrier relationship (QSABR) under the framework of theoretical quantum chemical descriptors calculated solely from the structures of diene and dienophile reactants. Experimental activation barrier data were obtained from literature. Descriptors were computed using Hartree-Fock theory using 6-31G(d) basis set as implemented in Gaussian 09 software.ResultsVariable selection and model development were carried out by stepwise multiple linear regression methodology. Predictive performance of the quantitative structure-activation barrier relationship (QSABR) model was assessed by training and test set concept and by calculating leave-one-out cross-validated Q2 and predictive R2 values. The QSABR model can explain and predict 86.5% and 80% of the variances, respectively, in the activation energy barrier training data. Alternatively, a neural network model based on back propagation of errors was developed to assess the nonlinearity of the sought correlations between theoretical descriptors and experimental reaction barriers.ConclusionsA reasonable predictability for the activation barrier of the test set reactions was obtained, which enabled an exploration and interpretation of the significant variables responsible for Diels-Alder interaction between dienes and dienophiles. Thus, studies in the direction of QSABR modelling that provide efficient and fast prediction of activation barriers of the Diels-Alder reactions turn out to be a meaningful alternative to transition state theory based computation.

Comparative QSAR and pharmacophore analysis for a series of 2,4-dihydro-3H-1,2,4-triazol-3-ones derivatives as angiotensin II AT1 receptor antagonists

This article describes the development of a robust two-dimensional (2D), Group-based quantitative structure–activity relationship (G-QSAR), k-nearest neighbor (kNN), and pharmacophore models and the investigation of SAR analysis of 42 triazolinone derivatives reported for angiotensin II (Ang II) AT1 receptor antagonists using VLife MDS software. Comparing with partial least square (PLS) methodology coupled with various feature selection methods, viz., stepwise and simulated annealing (SA) are applied for the optimization and selection of suitable descriptors for the development of QSAR model for triazolinone derivatives. The study correlates activity measured as pIC50 values of 42 structurally related triazolinone analogs to several physicochemical parameters representing spatial, electrostatic, hydrophobic, hydrogen bond acceptor (HAc) and donor (HDr), thermodynamic, and topological fields. The statistically significant best 2D-QSAR model by SA coupled with PLS analysis method showed 88.7 % variation in biological activity and q 2 = 0.7817 with pred_r 2 = 0.8003 with the descriptors like SddsN (nitro) count, SdsCHcount, SsBrcount, SsNH2-index, and SssNHE-index. The statistically significant best G-QSAR model having correlation coefficient r 2 = 0.7764 and cross-validated squared correlation coefficient q 2 = 0.6729 with external predictive ability of pred_r 2 = 0.7141 was developed by SA-PLS method. Molecular field analysis was used to construct the best 3D-QSAR model using SA-PLS method, showing good correlative and predictive capabilities in terms q 2 = 0.7811 and pred_r 2 = 0.7597. The results indicated that the steric and spatial parameters significantly influence antagonistic activity. Electron-withdrawing group at aryl moiety R1 and R3 positions was found to be an essential feature for Ang II AT1 receptor. In addition, the pharmacophore model well corroborated with kNN studies as the contours of latter were in good agreement with the 3D orientation of the pharmacophoric features. A five-point pharmacophore with three HAcs one HDr, and one AroC feature (aromatic) was obtained. This information is pertinent for the further design of new antihypertensive agent containing the triazolinone nucleus.

Structural requirements of N-aryl-oxazolidinone-5-carboxamide derivatives for anti-HIV protease activity using molecular modelling techniques

Two- and three-dimensional quantitative structure–activity relation (QSAR) models were generated for a series of N-aryl-oxazolidinone-5-carboxamide derivatives to identify potent anti-HIV-1 integrase ligands. The studies were performed by the partial least squares method coupled with various feature selection methods, such as genetic algorithm (GA) and stepwise methods, to derive QSAR models, which were further validated for statistical significance and predictive ability by internal and external validation. The best two-dimensional QSAR model was selected, which had a correlation coefficient, r2, of 0.9246, a cross-validated squared correlation coefficient, q2, of 0.7726 and an external predictive ability, pred_r2, of 0.8331. This model was developed by GA partial least squares with descriptors such as SsNH2 count, SssOE index, T_N_F_4, SsOH count and T_O_O_2. Three-dimensional QSAR studies gave reasonably good predictive models with a high cross-validated q2 value of 0.6953 and pred_r2 of 0.7499 in the GA k nearest neighbour molecular field analysis method. For a series of N-aryl-oxazolidinone-5-carboxamide derivatives, chemical feature-based pharmacophore models with the lowest root mean square deviation value (0.00765nm) showed one aromatic feature, two hydrogen bond acceptors, one hydrogen bond donor and one aliphatic feature. The information provided by two- and three-dimensional QSAR models may lead to better understanding of the structural requirements of oxazolidinone-5-carboxamides and help in designing novel, potent anti-HIV-1 integrase molecules.

Molecular modeling studies of substituted 3,4-dihydroxychalcone derivatives as 5-lipoxygenase and cyclooxygenase inhibitors

A fifty-four compound series of 5-lipoxygenase and cyclooxygenase inhibitory activity of substituted 3,4-dihydroxychalcones was subjected to the development of a robust quantitative structure–activity relationship (QSAR) and pharmacophore model and the investigation of structure–activity relationship analysis using Molecular Design Suite software version 3.5. The requirements for the 5-lipoxygenase and cyclooxygenase activity are explored with 2D, group based and k-Nearest Neighbor studies. Simulated annealing is applied as variable selection methods for an effective comparison and model development. Several statistical expressions were developed using partial least square (PLS) analysis. The best QSAR models were further validated by leave-one-out method of cross-validation. The statistically significant best 2D-QSAR model was selected, having correlation coefficient r2 = 0.9338, and cross-validated squared correlation coefficient q2 = 0.7832 with external predictive ability of pred_r2 = 0.8169 was developed by simulated annealing PLS with the descriptors like Average −ve potential, SsCH3E-index, SsClE-index, SsOH count, and HUMO Energy. Group based QSAR model indicates that molar refractivity and methoxy, ethoxy, carboxylic groups in R1 positions can enhance activity. The obtained 3D-QSAR (k-Nearest Neighbor) model using simulated annealing as a variable selection method has an excellent correlation coefficient value (r2 = 0.8537) along with good statistical significance as shown by high Fisher’s ratio (F = 73.86). The model also exhibits good predictive power confirmed by the high value of cross-validated correlation coefficient (q2 = 0.7841). The k-Nearest Neighbor contour maps suggest some important structural features like electronegative substituents which are essential for the activity exhibited by these compounds, and inclusion of electron-donating substituents will enhance the 5-lipoxygenase and cyclooxygenase inhibition activity. The pharmacophore analysis of the molecules demonstrated that the aromatic/aliphatic and hydrogen bond donor features are important pharmacophore contours favorable for these activities. The information rendered by 2D-QSAR, group based and 3D-QSAR models may lead to a better understanding of structural requirements of chalcone derivatives and also aid in designing novel potent 5-lipoxygenase and cyclooxygenase molecules.

CoMFA studies and in vitro evaluation of some 3-substituted benzylthio quinolinium salts as anticryptococcal agents

The 3-dimensional quantitative structure–activity relationship (3D-QSAR) molecular modeling technique or comparative molecular field analysis (CoMFA) has been used to design analogs of the natural product cryptolepine (1). Twenty-three compounds with their in vitro biological activities (IC50 values) against Crytococcus neoformans were used to generate the training set database of compounds for the CoMFA studies. The cross-validated q2, noncross-validated r2, and partial least squares (PLS) analysis results were used to predict the biological activity of 11 newly designed test set compounds. The best CoMFA model produced a q2 of 0.815 and an r2 of 0.976 indicating high statistical significance as a predictive model. The steric and electrostatic contributions from the contour map were interpreted from the color-coded contour plots generated from the PLS model and the active structural components for potency against C. neoformans were determined and validated in the test set compounds. The 3-substituted benzylthio quinolinium salts (4) that make up the test set were synthesized and evaluated based on the predicted activity from the CoMFA model and the results produced a good correlation between the predicted and experimental activity (R=0.82). Thus, CoMFA has served as an effective tool to aid the design of new analogs and in this case, it has aided the identification of compounds equipotent with amphotericin B, the gold standard in antifungal drug design.

Quantitative studies on structure–ORAC relationships of anthocyanins from eggplant and radish using 3D-QSAR

The 3-dimensional quantitative structure activity relationship (3D-QSAR) models were established from 21 anthocyanins based on their oxygen radical absorbing capacity (ORAC) and were applied to predict anthocyanins in eggplant and radish for their ORAC values. The cross-validated q2=0.857/0.729, non-cross-validated r2=0.958/0.856, standard error of estimate=0.153/0.134, and F=73.267/19.247 were for the best QSAR (CoMFA/CoMSIA) models, where the correlation coefficient r2pred=0.998/0.997 (>0.6) indicated a high predictive ability for each. Additionally, the contour map results suggested that structural characteristics of anthocyanins favourable for the high ORAC. Four anthocyanins from eggplant and radish have been screened based on the QSAR models. Pelargonidin-3-[(6′′-p-coumaroyl)-glucosyl(2→1)glucoside]-5-(6′′-malonyl)-glucoside, delphinidin-3-rutinoside-5-glucoside, and delphinidin-3-[(4′′-p-coumaroyl)-rhamnosyl(1→6)glucoside]-5-glucoside potential with high ORAC based the QSAR models were isolated and also confirmed for their relative high antioxidant ability, which might attribute to the bulky and/or electron-donating substituent at the 3-position in the C ring or/and hydrogen bond donor group/electron donating group on the R1 position in the B ring.

A 3D QSAR Study of Betulinic Acid Derivatives as Anti-Tumor Agents Using Topomer CoMFA: Model Building Studies and Experimental Verification

Betulinic acid (BA) is a natural product that exerts its cytotoxicity against various malignant carcinomas without side effects by triggering the mitochondrial pathway to apoptosis. Betulin (BE), the 28-hydroxyl analog of BA, is present in large amounts (up to 30% dry weight) in the outer bark of birch trees, and shares the same pentacyclic triterpenoid core as BA, yet exhibits no significant cytotoxicity. Topomer CoMFA studies were performed on 37 BA and BE derivatives and their in vitro anti-cancer activity results (reported as IC50 values) against HT29 human colon cancer cells in the present study. All derivatives share a common pentacyclic triterpenoid core and the molecules were split into three pieces by cutting at the C-3 and C-28 sites with a consideration toward structural diversity. The analysis gave a leave-one-out cross-validation q2 value of 0.722 and a non-cross-validation r2 value of 0.974, which suggested that the model has good predictive ability (q2 > 0.2). The contour maps illustrated that bulky and electron-donating groups would be favorable for activity at the C-28 site, and a moderately bulky and electron-withdrawing group near the C-3 site would improve this activity. BE derivatives were designed and synthesized according to the modeling result, whereby bulky electronegative groups (maleyl, phthalyl, and hexahydrophthalyl groups) were directly introduced at the C-28 position of BE. The in vitro cytotoxicity values of the given analogs against HT29 cells were consistent with the predicted values, proving that the present topomer CoMFA model is successful and that it could potentially guide the synthesis of new betulinic acid derivatives with high anti-cancer activity. The IC50 values of these three new compounds were also assayed in five other tumor cell lines. 28-O-hexahydrophthalyl BE exhibited the greatest anti-cancer activities and its IC50 values were lower than those of BA in all cell lines, excluding DU145 cells.

3D-QSAR CoMFA studies on sulfonamide inhibitors of the Rv3588c β-carbonic anhydrase from Mycobacterium tuberculosis and design of not yet synthesized new molecules

The human pathogen Mycobacterium tuberculosis contains three β-carbonic anhydrases (CAs, EC 4.2.1.1) in its genome. Inhibition of some of these CAs was shown to modulate the growth of M. tuberculosis. 3D-QSAR Comparative molecular field analyses (CoMFA) were carried out on inhibitors of the enzyme Rv3588c (also denominated mtCA 2). A series of sulfonamides known to inhibit mtCA 2, including some diazenylbenzenesulfonamides, was considered in our study. The predictive ability of the model was assessed using a test set of seven compounds. The best model has demonstrated a good fit having predictive r2 value of 0.93 and cross-validated coefficient q2 value as 0.88 in tripos CoMFA region. Our results indicate that the steric and electrostatic factors play a significant role in mtCA 2 inhibition for the investigated compounds. We proposed nine new not yet synthesized mtCA 2 inhibitors, all of them probably with significantly improved anti-Rv3588c inhibitory activity.

3D-QSAR study on 2,3-dihydroimidazo[4,5]-pyridin-2-one derivatives with a meta substitution pattern as V600EBRAF inhibitors

V600EBRAF have been identified as new promising targets for the design of novel anticancer agents. It is reported that the inhibitors based on 2,3-dihydroimidazo[4,5]pyridin-2-one scaffold and a meta-substituted middle ring exhibit potent inhibitory activities toward V600EBRAF. To investigate how their chemical structures relate to the inhibitory activities and to identify the key structural elements that are required in the rational design of potential drug candidates of this class, molecular docking simulations and three-dimensional quantitative structure–activity relationship (3D-QSAR) methods were performed. The bioactive conformation was explored by docking one potent compound 1 into the active site of BRAF in its DFG-out inactive conformation. The constructed CoMFA and CoMSIA models produced statistically significant results with the cross-validated correlation coefficients q2 of 0.563 and 0.624, non-cross-validated correlation coefficients r2 of 0.982 and 0.962, and predicted correction coefficients rpred2 of 0.822 and 0.875, respectively. In addition, the CoMFA and CoMSIA models were used to guide the design of a series of new inhibitors of this class with predicted excellent activities. Thus, these models may be used as an efficient tool to predict the inhibitory activities and to guide the future rational design of 2,3-dihydroimidazo[4,5]pyridin-2-one derivatives with a meta substitution pattern as BRAF inhibitors with potent activities.

Three Dimensional Quantitative Structure-Activity Relationship of 5H-Pyrido[4,3-b]indol-4-carboxamide JAK2 Inhibitors

Janus kinase 2 (JAK2) is an intracellular nonreceptor tyrosine kinase that belongs to the JAK family of kinases, which play an important role in survival, proliferation, and differentiation of a variety of cells. JAK2 inhibitors are potential drugs for the treatment of myeloproliferative neoplasms. The three dimensional quantitative structure-activity relationships have been studied on a series of JAK2 inhibitors by comparative molecular field analysis (CoMFA), and comparative molecular similarity indices analysis (CoMSIA). The CoMFA model had a cross-validated coefficient q2 of 0.633, and the relation non-cross-validated coefficient r2 of 0.976. The F value is 225.030. The contributions of steric and electrostatic fields to the activity are 55.2% and 44.8%, respectively. For the CoMSIA study, the q2, r2, and F values of the model are 0.614, 0.929, and 88.771, respectively. The contributions of steric, electrostatic, hydrophobic, hydrogen bond donor, and hydrogen bond donor fields to the activity are 27.3%, 23.9%, 16.4%, 21.7%, and 10.7%, respectively. The CoMFA and CoMSIA models showed strong predictive ability, and the 3D contour plots give the basis on the structure modification of JAK2 inhibitors.

Pharmacophore and QSAR modeling of some structurally diverse azaaurones derivatives as anti-malarial activity

Quantitative structure–activity relationship (QSAR) and Pharmacophore studies were performed on a series of 35 azaaurones derivatives to find out the structural requirements of their antimalarial activities. The compounds in the selected series were characterized by spatial, molecular and electrotopological descriptors using QSAR module of molecular design suite (V-Life MDSTM 3.5). Correlations between inhibitory activities and calculated predictor variables were established through partial least square regression method. The developed QSAR models were found to be statistically significant with respect to training (r 2 > 0.6), and cross-validation (q 2 > 0.6). Simulated annealing (SA) and stepwise (SW) regression are applied as variable selection methods for an effective comparison and model development. This study was performed with 35 compounds (data set) using the sphere exclusion (SE) algorithm method for the division of the data set into training and test sets. The statistically significant 2D-QSAR model having r 2 = 0.9061 and q 2 = 0.8150 with pred_r 2 = 0.8719 was developed by SW-PLS confirms a positive contribution of T_O_Cl_6, T_C_Cl_1, SsOHcount, T_C_F_2 and SsCH3E index to the anti-malarial activity and best Group-based QSAR (GQSAR) model having r 2 = 0.7624 and q 2 = 0.7341 with pred_r 2 = 0.7461 was developed by SA-PLS. The result two-dimensional QSAR and GQSAR clearly explained that substitution of topological indices, hydrophobic properties and auto-correlation descriptors of different atomic properties at R1 and R2 on aurone ring is essential for the activity. Further analysis using three-dimensional QSAR technique identifies a suitable model obtained by SA-partial least square method leading to anti-malarial activity prediction. Molecular field analysis was used to construct the best 3D-QSAR model using k-nearest neighbor method, showing good correlative and predictive capabilities in terms of q 2 = 0.6906 and pred_r 2 = 0.7454. Additionally the pharmacophore model well corroborated with k-nearest neighbor studies as the contours of later were in good agreement with the 3D orientation of the pharmacophoric features. The distances between the pharmacophore sites were measured in order to confirm their significance to the activities. The results reveal that the acceptor, donor, and aromatic/hydrophobic pharmacophore properties are favorable contours sites for the activities. The results of 2D-QSAR, GQSAR and k-nearest neighbor-based 3D-QSAR studies give detailed structural insights as well as highlights important binding features of these substituted azaaurones derivatives as anti-malarial activity, which can provide guidance for the rational design of novel potent azaaurones derivatives.

Two- and three-dimensional QSAR studies on a set of antimycobacterial pyrroles: CoMFA, Topomer CoMFA, and HQSAR

Designing compounds having good anti-tubercular activity is gaining much importance in the area of tuberculosis research due to the reemergence of antibiotic resistance strains. In this study, quantitative structure activity relationship (QSAR) of a group of 55 anti-tubercular agents with varying structures and potencies, CoMFA, Topomer CoMFA, and HQSAR studies have been performed on a series of antimycobacterial pyrroles. A training set containing 46 compounds served to establish the model and CoMFA was assessed by r 2 value of 0.852 and cross-validated the q 2 value of 0.614. In the Topomer CoMFA, r 2 was 0.805, whole cross-validated q 2 was 0.535. The predictive ability of CoMFA and Topomer CoMFA models was determined using a test set of 9 compounds, which gave the predictive correlation coefficients (r pred 2 ) of 0.623 and 0.458, respectively. HQSAR was also carried out as a complementary study, and the best HQSAR model was generated using atoms, bond, connectivity, and hydrogen atoms as fragment distinction, 7–10 as fragment size and three components showing the cross-validated q 2 value of 0.710 and conventional r 2 value of 0.795. CoMFA, Topomer CoMFA steric, electrostatic, and HQSAR atomic contribution maps were generated to analyze the structural features of the datasets on that govern their inhibitory potency. 3D and 2D-QSAR studies (CoMFA, Topomer CoMFA, HQSAR) of antimycobacterial pyrroles are described.

Combined 3D-QSAR modeling and molecular docking study on azacycles CCR5 antagonists

The beta chemokine receptor 5 (CCR5) is an attractive target for pharmaceutical industry in the HIV-1, inflammation and cancer therapeutic areas. In this study, we have developed quantitative structure activity relationship (QSAR) models for a series of 41 azacycles CCR5 antagonists using comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), and Topomer CoMFA methods. The cross-validated coefficient q2 values of 3D-QASR (CoMFA, CoMSIA, and Topomer CoMFA) methods were 0.630, 0.758, and 0.852, respectively, the non-cross-validated R2 values were 0.979, 0.978, and 0.990, respectively. Docking studies were also employed to determine the most probable binding mode. 3D contour maps and docking results suggested that bulky groups and electron-withdrawing groups on the core part would decrease antiviral activity. Furthermore, docking results indicated that H-bonds and π bonds were favorable for antiviral activities. Finally, a set of novel derivatives with predicted activities were designed.