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Kunal Roy

Publications and source records attributed to Kunal Roy.

At least 19 recordsLinked to original sources
PubMed2006

Comparative QSAR modeling of CCR5 receptor binding affinity of substituted 1-(3,3-diphenylpropyl)-piperidinyl amides and ureas.

The present QSAR study attempts to explore the structural and physicochemical requirements of substituted 1-(3,3-diphenylpropyl)-piperidinyl amides and ureas for CCR5 binding affinity using linear free energy-related (LFER) model of Hansch. QSAR models have been developed using electronic (Hammett sigma), hydrophobicity (pi), and steric (molar refractivity and STERIMOL L, B1, and B5) parameters of phenyl ring substituents of the compounds along with appropriate dummy variables. Whole molecular descriptor like partition coefficient (logP(calcd)) was also tried as an additional descriptor. Statistical techniques like stepwise regression, multiple linear regression with factor analysis as the data preprocessing step (FA-MLR), partial least squares with factor analysis as the preprocessing step (FA-PLS), principal component regression analysis (PCRA), multiple linear regression with genetic function approximation (GFA-MLR), and genetic partial least squares (G/PLS) were applied to identify the structural and physicochemical requirements for the CCR5 binding affinity. The generated equations were statistically validated using leave-one-out technique. The quality of equations obtained from stepwise regression, FA-MLR, FA-PLS, and PCRA is of acceptable statistical range (explained variance ranging from 71.9% to 80.4%, while predicted variance ranging from 67.4% to 77.0%). The GFA-derived models show high intercorrelation among predictor variables used in the equations while the G/PLS model shows lowest statistical quality among all types of models. The best models were also subjected to leave-25%-out crossvalidation.

Amides↗
PubMed2006

The HIV entry inhibitors revisited.

The new generation of antiviral drugs intended to counter HIV-1 entry into susceptible cells is emerging swiftly. The antiviral agents that inhibit HIV entry to the target cells (denoted as HIV entry inhibitors) are already in different phases of clinical trials. Operating early in the viral life cycle, they prevent viral entry, and have a novel, highly specific mechanism of action with a low toxicity profile. Entry inhibitors have different toxicity and resistance profiles than the existing reverse transcriptase and protease inhibitors. Some of these compounds demonstrated in vitro synergism with other classes of antivirals, thus offering the rationale for their combination in therapies for HIV-infected individuals. It is worth focusing on recent developments in HIV entry inhibitors, as most of the current drug regimens suffer from the events of developing resistance against existing combination therapies. Recent advances in the understanding of the cellular and molecular mechanisms of HIV-1 entry provide the basis for novel therapeutic strategies that prevent viral penetration of the target cell-membrane, while reducing detrimental virus and treatment effects on cells and prolonging virion exposure to immune defenses. A number of potential sites for therapeutic intervention become accessible during the narrow window between virus attachment and the subsequent fusion of viral envelope with the cell membrane. The HIV-1 coreceptors are particularly attractive from the perspective of identifying new antiviral compounds, since they are seven-transmembrane motif G protein-coupled receptors (GPCRs), a family of proteins that is a well-validated target for drug development. Among the many chemokine receptors that can mediate HIV-1 entry in vitro, only CCR5 and CXCR4 are of frontline pharmacological importance. In particular, CCR5 is essential for viral transmission and replication during the early and clinically latent phase of disease. Several small-molecule antagonists of CCR5 and CXCR4 that block chemokine binding and HIV-1 entry have been identified in recent years. Considerable advances have been made in the last years in the design of derivatives acting as inhibitors of HIV entry. The molecular mechanism involved in viral entry, the structural and functional aspects of entry inhibitors are reviewed here. We have also summarized the recent insights into how small-molecule antagonists interact with CCR5 and CXCR4, focusing on drug development programs that are well documented in the scientific literature. An overview of the entry inhibitors that are in preclinical or early clinical development, and the Quantitative Structure-Activity Relationships (QSAR) studies reported for the coreceptor antagonists are also be presented.

Animals↗
PubMed2006

Exploring selectivity requirements for peripheral versus central benzodiazepine receptor binding affinity: QSAR modeling of 2-phenylimidazo[1,2-a]pyridine acetamides using topological and physicochemical descriptors.

Considering the potential of peripheral benzodiazepine receptor (PBR) ligands in therapeutic applications and clinical benefit in the management of a large spectrum of different indications, quantitative structure-activity relationship (QSAR) study has been attempted to explore the structural and physicochemical requirements for selectivity of 2-phenylimidazo[1,2-a]pyridineacetamides for binding with peripheral over central benzodiazepine receptors (CBRs). For PBR binding affinity, molar refractivity (MR) shows a parabolic relation with binding affinity suggesting that binding affinity increases with increase in volume of the compounds, until it reaches the critical value, after which the affinity decreases. The negative coefficients of S_aaN and S_ssNH indicate that binding affinity increases with decrease in E-state value of (N/) (aromatic nitrogen) and HN< (secondary amino group) fragments. The coefficient of 3XVC and JX term indicates the importance of shape and branching for binding affinity. For CBR binding affinity, lipophilicity of molecules is detrimental to the binding affinity, while presence of hydrogen at Y position is conducive to the activity. Selectivity pattern of these ligands for peripheral (cortex) over central receptors requires the presence and absence of methyl group at R2 and R3 positions respectively, and shows the importance of MR and shape parameter. Similarly, selectivity of these ligands for peripheral (ovary) over central receptors requires the presence and absence of methyl group at R2 and R3 positions respectively, presence of phenyl group at R1 and R2 positions and selectivity relation shows importance of MR, shape and branching.

Acetamides↗
PubMed2005

QSTR with extended topochemical atom (ETA) indices. VI. Acute toxicity of benzene derivatives to tadpoles (Rana japonica).

structure-toxicity relationship (QSTR) studies have proved to be a valuable approach in research on the toxicity of organic chemicals for ranking chemical substances with respect to their potential hazardous effects on living systems. With this background, we have modeled here the acute lethal toxicity of 51 benzene derivatives with recently introduced extended topochemical atom (ETA) indices [Roy and Ghosh, Internet Electron J Mol Des 2:599-620 (2003)]. We also compared the ETA relations with non-ETA models derived from different topological indices (Wiener W, Balaban J, flexibility index, Hosoya Z, Zagreb, molecular connectivity indices, E-state indices and kappa shape indices) and physicochemical parameters (AlogP98, MolRef,H_bond_donor and H_bond_acceptor). Genetic function approximation (GFA) and factor analysis (FA) were used as the data-preprocessing steps for the development of final multiple linear regression (MLR) equations. Principal-component regression analysis (PCRA) was also used to extract the total information from the ETA/non-ETA/combined matrices. All the models developed were cross-validated using leave-one-out (LOO) and leave-many-out techniques. The summary of the statistics of the best models is as follows: (1) FA-MLR: ETA model- Q 2 (LOO)=0.852, R 2=0.894; non-ETA model- Q 2=0.782, R 2=0.835; ETA + non-ETA model-Q 2 =0.815, R 2=0.859. (2) GFA-MLR: ETA model-Q 2 =0.847, R 2=0.915; non-ETA model-Q 2 =0.863, R 2=0.898; ETA + non-ETA model-Q 2 =0.859, R 2=0.893. 3. PCRA: ETA model-Q 2 =0.864, R 2=0.901; non-ETA model- Q 2=0.866, R 2=0.922; ETA + non-ETA model-Q 2=0.846, R 2=0.890. The statistical quality of the ETA models is comparable to that of non-ETA models. Again, use of non-ETA descriptors in addition to ETA descriptors does not increase the statistical acceptance of the relations significantly. The predictive potential of these models was better than that of the previously reported models using physicochemical parameters [Huang et al., Chemosphere 53:963-970 (2003)]. The relations from ETA descriptors suggest a parabolic dependence of the toxicity on molecular size. Furthermore, the toxicity increases with functionality contribution of chloro substituent and decreases with those of methoxy, hydroxy, carboxy and amino groups. This study suggests that ETA parameters are sufficiently rich in chemical information to encode the structural features that contribute significantly to the acute toxicity of benzene derivatives to Rana japonica.

Acute Disease↗
PubMed2005

QSAR by LFER model of HIV protease inhibitor mannitol derivatives using FA-MLR, PCRA, and PLS techniques.

The present quantitative structure-activity relationship (QSAR) study attempts to explore the structural and physicochemical requirements of mannitol derivatives for HIV protease inhibitory activity using linear free energy related model of Hansch. QSAR models have been developed using electronic (Hammett sigma), hydrophobicity (pi), and steric (molar refractivity and STERIMOL L, B1, and B5) parameters of phenyl ring substituents of the compounds along with appropriate dummy variables. Whole molecular descriptors like partition coefficient (logP(calcd)) and molar refractivity (MR) were also tried as additional descriptors. Statistical techniques like stepwise regression, multiple linear regression with factor analysis as the data preprocessing step (FA-MLR), principal component regression analysis (PCRA), and partial least squares (PLS) analysis were applied to identify the structural and physicochemical requirements for HIV protease inhibitory activity. The generated equations were statistically validated using leave-one-out technique. The quality of equations obtained from stepwise, FA-MLR, PCRA, and PLS are of acceptable statistical range (explained variance ranging from 74.0% to 80.5%, while predicted variance ranges from 70.3% to 77.1%). The coefficient of molar refractivity shows that the activity decreases with increase in volume. Lipophilicity of the para substituents at Y position is conducive to the activity while lipophilicity of the para substituents at X position is detrimental to the activity. The coefficients of molar refractivity (mr(Y_p)) and STERIMOL parameters for para substituents at X and Y positions (B1(X_p) and B5(Y_p)) of the phenyl rings indicate that the width of the substituents at X position and the overall size of para substituents at Y position are the detrimental factors for the activity. The fluoro substituent at ortho position (Y) decreases the activity when compared to the corresponding unsubstituted congener. Presence of hydrogen bond donor groups at para position (Y) also reduces the activity. Additionally, presence of substituent at ortho position (X) and the presence of substituent at para position (Y) are conducive for the activity. Presence of fluorine at X and Y positions also increases the activity.

Chemical Phenomena↗
PubMed2005

QSAR of adenosine A3 receptor antagonist 1,2,4-triazolo[4,3-a]quinoxalin-1-one derivatives using chemometric tools.

Considering the potential of selective adenosine A3 receptor subtype ligands in the development of prospective therapeutic agents, an attempt has been made to explore physicochemical requirements of 1,2,4-triazolo[4,3-a]quinoxalin-1-one derivatives for A3 receptor binding. In this study, lipophilicity (logP), physicochemical substituent constants (pi, MR, sigma p) of phenyl ring substituents, and Wang-Ford charges of common atoms of the quinoxaline nucleus (calculated from molecular electrostatic potential surface of energy-minimized geometry using AM1 technique) were used as independent variables along with suitable dummy parameters. The best multiple linear regression (MLR) equation obtained from factor analysis (FA-MLR) as the preprocessing step could explain and predict 72.6% and 65.3%, respectively, of the variance of the binding affinity. The same equation also emerged as the best equation in the population of 100 equations obtained from genetic function approximation (GFA-MLR). The results suggested that presence of an electron-withdrawing group at the para position of the phenyl ring would be favorable for the binding affinity. Again, the presence of a nitro group at position R1 increases the binding affinity. When factor scores were used as predictor variables in the principal component regression analysis, the resultant model showed 78.6% explained variance and 63.1% predicted variance. The best equation derived from G/PLS could explain and predict 74.4% and 64.8%, respectively. The results have suggested the importance of Wang-Ford charges of atoms C15 and C19, apart from positive contributions of electron-withdrawing para substituents of the variance of the phenyl ring and nitro group at the R1 position.

Adenosine A3 Receptor Antagonists↗
PubMed2005

Exploring 3D-QSAR of thiazole and thiadiazole derivatives as potent and selective human adenosine A3 receptor antagonists+.

Binding affinity data [Bioorg Med Chem (2004) 12:613-623] of thiazole and thiadiazole derivatives (n = 30) for the human adenosine A3 receptor subtype have been subjected to 3D-QSAR (Quantitative structure-activity relationships) analyses by molecular shape analysis (MSA) and molecular field analysis (MFA) techniques using Cerius2 Version 4.8. In the case of the MSA, the major steps were (1) generation of conformers and energy minimization; (2) hypothesizing an active conformer (global minimum of the most active compound); (3) selecting a candidate shape-reference compound (based on the active conformation); (4) performing pairwise molecular superimposition using the maximum common subgroup (MCSG) method; (5) measuring molecular shape commonality using MSA descriptors; (6) determining other molecular features by calculating spatial, electronic and conformational parameters; (7) selection of conformers; (8) generation of QSAR equations by genetic function algorithm (GFA) or stepwise regression. The best 3D-QSAR equation (MSA) obtained from GFA technique shows 70.0% predicted variance (leave-one-out) and 77.7% explained variance. This equation shows the importance of Jurs descriptors (atomic charge weighted positive surface area, relative negative charge and relative positive charge surface area), partial moment of inertia, energy of the most stable conformer and the ratio of common overlap steric volume to volume of individual molecules. In the case of stepwise regression, the best relation showed 46.1% predicted variance and 72.3% explained variance. In the case of MFA, the major steps were (1) generating conformers and energy minimization; (2) matching atoms using a maximum common substructure (MCS) search and aligning molecules using the default options; (3) setting MFA preferences (rectangular grid with 2 A step size, charges by the Gasteiger algorithm, H+ and CH3 as probes); (4) creating the field; (5) analysis by the Genetic partial least squares (G/PLS) method. The equation obtained was of excellent statistical quality: 96.1% explained variance and 71.6% predicted variance. Statistically reliable 3D-QSAR models obtained from this study suggest that these techniques could be useful to design potent A3 receptor antagonists.

Adenosine A3 Receptor Antagonists↗
PubMed2005

QSAR by LFER model of cytotoxicity data of anti-HIV 5-phenyl-1-phenylamino-1H-imidazole derivatives using principal component factor analysis and genetic function approximation.

Cytotoxicity data of anti-HIV 5-phenyl-1-phenylamino-1H-imidazole derivatives were subjected to quantitative structure-activity relationship (QSAR) study using linear free energy related (LFER) model of Hansch using electronic (Hammett sigma), hydrophobicity (pi) and steric (molar refractivity and STERIMOL L, B1, B2, B3 and B4) parameters of phenyl ring substituents of the compounds, along with appropriate indicator variables. Principal component factor analysis (FA) was used as the data-preprocessing step to identify the important predictor variables contributing to the response variable and to avoid collinearities among them. The generated multiple linear regression (MLR) equations were statistically validated using leave-one-out technique. Genetic function approximation (GFA) was also used on the same data set to develop QSAR equations, which produced the same best equation as obtained with FA-MLR. The final equation is of acceptable statistical quality (explained variance 80.2%) and predictive potential (leave-one-out predicted variance 74%). The analysis explores the structural and physicochemical contributions of the compounds for cytotoxicity. A thiol substituent at 2 position of the imidazole nucleus decreases cytotoxicity when compared to the corresponding unsubstituted congener. Presence of hydrogen bond donor group at meta position of the phenyl ring present at 5 position of the imidazole nucleus also reduces cytotoxicity. Additionally, absence of any substituent at 2 and 3 positions of the phenyl ring of 1-phenylamino fragment reduces the cytotoxicity. The negative coefficient of sigmap indicates that presence of electron-withdrawing substituents at the para position of the phenyl ring of the 1-phenylamino fragment is not favourable for the cytotoxicity. Again, lipophilicity of meta substituents of the 5-phenyl ring increases cytotoxicity. The coefficients of molar refractivity (MRm) and STERIMOL parameters for meta substituents (Lm, B1m and B4m) of the phenyl ring of 1-phenylamino fragment indicate that the length, width and overall size of meta substituents are conducive factors for the cytotoxicity.

Algorithms↗
PubMed2005

QSTR with extended topochemical atom indices. Part 5: Modeling of the acute toxicity of phenylsulfonyl carboxylates to Vibrio fischeri using genetic function approximation.

In continuation of our recent efforts to model the acute toxicity of 56 phenylsulfonyl carboxylates to Vibrio fischeri using principal component factor analysis, the present paper deals with modeling of the same data set with extended topochemical atom (ETA) indices using genetic function approximation (GFA) as the statistical tool. The statistical quality of the best model using ETA descriptors is as follows: n=56, Q2=0.771, Ra2=0.861, R=0.935, F=69.0 (df5,50), s=0.172, AVRES=0.128. This equation was better in statistical quality than that obtained previously using principal component factor analysis as the data-preprocessing step. An attempt was also made to model the data set with different nonETA parameters (topological indices including Wiener, Hosoya Z, molecular connectivity, kappa shape, Balaban J and E-State parameters apart from physicochemical parameters like AlogP98, MolRef and H_bond_acceptor) and the best model showed the following quality: n=56, Q2=0.805, Ra2=0.820, R=0.913, F=53.5 (df4,51), s=0.196, AVRES=0.136. An attempt to use both ETA and nonETA parameters lead to an equation which is marginally better than the best ETA model: n=56, Q2=0.779, Ra2=0.865, R=0.937, F=71.7 (df5,50), s=0.169, AVRES=0.127. Use of the ETA indices suggested negative contributions of steric bulk, functionality of C10, volume of substituents on C10 and functionalities of chloro and nitro substituents at X1 position and positive contributions of functionalities of C4 and C1 and presence of substituents with electronegative atoms, especially at R2 and R3 positions. Furthermore, absence of substituents at R2 and R3 positions decreases toxicity. This study corroborates the results obtained from principal component factor analysis and suggests that ETA parameters are sufficiently rich in chemical information to encode the structural features contributing significantly to the acute toxicity of phenylsulfonyl carboxylates to V. fischeri.

Aliivibrio fischeri↗
PubMed2005

Exploring QSAR of thiazole and thiadiazole derivatives as potent and selective human adenosine A3 receptor antagonists using FA and GFA techniques.

Binding affinity data of thiazole and thiadiazole derivatives (n=30) for human adenosine A3 receptor subtype have been subjected to Quantitative Structure-Activity Relationship (QSAR) analysis using quantum chemical and hydrophobicity parameters. Wang-Ford charges of the common atoms of the compounds [calculated from molecular electrostatic potential surface of energy minimized geometry using Austin Model 1 (AM1) technique] were used as independent variables apart from partition coefficient (logP) and suitable dummy parameters. The variables for the multiple regression analyses were selected based on principal component factor analysis (FA), and generated equations were statistically validated using leave-one-out technique. The best equation thus obtained explained and predicted 74.4% and 68.9% respectively of the variance of the binding affinity. The results suggested importance of Wang-Ford charges of atoms C2, C5 and C7. Furthermore, the A3 binding affinity increases with decrease of lipophilicity of the compounds and in the presence of methyl or ethyl substituent at R position. Again, the binding affinity decreases in the presence of tert-butyloxy group at R position. When factor scores were used as predictor variables in principal component regression analysis, the resulted model showed 87.0% predicted variance and 89.5% explained variance. The data set was also modeled using genetic function approximation (GFA) technique. The best two equations derived from GFA show better predicted variance values (0.753 and 0.739) than that found in case of the best equation derived from FA. However, considerable intercorrelation was found between two predictor variables in case of GFA derived equations. GFA derived equations show importance of Wang-Ford charges of different atoms of the thiazole/thiadiazole nucleus and phenyl ring (S9, X8 and C2, the effects of the first two being predominant) along with similar impact of lipophilicity and R group on the binding affinity as found in case of the FA derived relation.

Adenosine A3 Receptor Antagonists↗
PubMed2005

QSAR of estrogen receptor modulators: exploring selectivity requirements for ER(alpha) versus ER(beta) binding of tetrahydroisoquinoline derivatives using E-state and physicochemical parameters.

Considering importance of developing selective estrogen receptor modulators (SERMs), the present paper explores selectivity requirements of tetrahydroisoquinoline derivatives for binding with ER(alpha) versus ER(beta) receptors using E-state index and physicochemical parameters. The best model [n=21, Q(2)=0.512, R(a)(2)=0.613, R=0.819, F=11.6 (df 3,17)] for ER(alpha) binding data obtained from radioligand binding assay showed importance of C(1), C(15) and lipophilicity (logP) while the best model [n=21, Q(2)=0.768, R(a)(2)=0.796, R=0.904, F=40.1 (df 2,18)] for ER(beta) binding data showed importance of C(1) and molar refractivity (MR). While modeling ER(alpha)/ER(beta) selectivity [n=21, Q(2)=0.695, R(a)(2)=0.739, R=0.882, F=19.8 (df 3,17)], C(1), C(15) and molar refractivity were found to be significant contributors. The data obtained from cellular transcription assay were also modeled. In case of ER(alpha), the best equation involving E-state values of C(1) and C(14) and logP explained 62.1% of the variance while the best equation for ER(beta) involving E-state values of C(1) and C(15) and MR explained 64.6% of the variance of the response variable. In case of ER(alpha)/ER(beta) selectivity, the best equation involving E-state values of O(8), C(14) and N(27) showed 48.3% explained variance, which increased to 63.5% on deletion of single outlier. From the analysis it appears that the nitrogen atom of the aminoethoxyphenyl substituent and 6-hydroxy substituent of the tetrahydroisoquinoline nucleus play important roles for ER(alpha)/ER(beta) selectivity in addition to R(1) and R(2) substituents.

Estrogen Receptor alpha↗
PubMed2005

QSPR modeling of the water solubility of diverse functional aliphatic compounds by optimization of correlation weights of local graph invariants.

The optimization of correlation weights scheme was used to model the water solubility (ln S) of diverse functional aliphatic compounds (n = 193). The optimized descriptor formulated based on the data of a training set (n = 96) generated statistically acceptable relations for the training set (r2 = 0.987), test set (n = 97; r2 = 0.986) and combined set (r2 = 0.987). When the relation of ln S values with the optimized molecular descriptor formulated based on the data of the training set was used for the calculation of ln S values of the training set, r(pred)2 value was found to be satisfactory (0.988), which is indicative of the predictive potential of the scheme. The results indicate the promising potential of the optimization of correlation weights scheme in modeling studies.

Alcohols↗
PubMed2004

Exploring QSAR with E-state index: selectivity requirements for COX-2 versus COX-1 binding of terphenyl methyl sulfones and sulfonamides.

An attempt has been made to explore selectivity requirements for cyclooxygenase-2 (COX-2) versus cyclooxygenase-1 (COX-1) binding of terphenyl methyl sulfones and sulfonamides using electrotopological state (E-state) index and suitable indicator parameters. Multiple linear regression analyses produced statistically acceptable equations: the best relation based on 'all-possible-subsets regression' for COX-1 binding (n=18) showed predicted variance and explained variance of 0.675 and 0.777, respectively, while in case of the best equation for COX-2 binding (n=38), these values rose to 0.842 and 0.874, respectively. For the selectivity relation (n=17), predicted variance and explained variance values were 0.601 and 0.687, respectively. Based on the results of the analyses, three important sites have been suggested: sites A (methylsulfonyl or aminosulfonyl moiety), B (central phenyl ring), and C (terminal phenyl ring containing different substituents). All three sites are important for COX-2 binding while sites B and C are important for COX-1 binding. For COX-2 selectivity, only site C plays an important role. The study shows the utility of E-state index in developing statistically acceptable model having direct physicochemical significance.

Binding Sites↗
PubMed2004

QSAR of adenosine receptor antagonists. Part 3: Exploring physicochemical requirements for selective binding of 1,2,4-triazolo[5,1-i]purine derivatives with human adenosine A3 receptor subtype.

Considering potential of selective adenosine A3 receptor antagonists in the development of prospective therapeutic agents, an attempt has been made to explore selectivity requirements of 1,2,4-triazolo[5,1-i]purine derivatives for binding with cloned human adenosine A3 receptor subtype. In this study, partition coefficient (logP) values of the molecules (calculated by Crippen's fragmentation method) and Wang-Ford charges of the common atoms of the triazolopurine nucleus (calculated from molecular electrostatic potential surface of energy minimized geometry using AM1 technique) were used as independent variables along with suitable dummy parameters. The best equation describing A3 binding affinity [n=29, Q2=0.796, Ra2=0.853, R2=0.874, R=0.935, s=0.342, F=41.5 (df 4,24), SDEP=0.396] showed parabolic relation with logP (optimum value being 4.134). Further, it was found that an aromatic substituent conjugated with the triazole nucleus should be present at R2 position for A3 binding affinity. Again, high negative charges on N2 and N4 are conducive to the binding affinity. While exploring selectivity requirements of the compounds for binding with A3 receptor over that with A2A receptor, the selectivity relation [n=23, Q2=0.909, Ra2=0.918, R2=0.933, R=0.966, s=0.401, F=62.4 (df 4,18), SDEP=0.412] showed that an aromatic R2 substituent conjugated with the triazole nucleus contributes significantly to the selectivity. Again, presence of a 4-substituted-phenyl ring (except 4-OH-phenyl and 4-CH3-phenyl) at R2 position also increases selectivity. Further, charge difference between N2 and N11 (negative charge on the former should be higher and that on the latter should be less) contributes significantly to the selectivity. In addition, negative charge on N7 is conducive while presence of substituents like propyl, butyl, pentyl or phenyl at R1 position is detrimental for the A3 selectivity.

Adenosine A3 Receptor Antagonists↗
PubMed2004

Exploring QSAR of melatonin receptor ligand benzofuran derivatives using E-state index.

Considering the recent challenge to the medicinal chemists for the development of selective melatonin receptor ligands, an attempt has been made to explore physicochemical requirements of benzofuran derivatives for binding with human MT1 and MT2 receptor subtypes and also to explore selectivity requirements. In this study, E-states of different common atoms of the molecules (calculated according to Kier and Hall) and physicochemical parameters (partition coefficient and molar refractivity) were used as independent variables along with suitable dummy parameters. The best equation describing MT1 binding affinity [n = 34, Q2 = 0.670, Ra2 = 0.790, R2 = 0.822, R = 0.907, s = 0.609, F = 25.8 (df 5, 28)] suggests that the binding affinity decreases as the value of n (number of CH2 spacer beside R2) increases while it increases with rise in electrotopological state values of different atoms of the benzofuran ring. Again, presence of methoxy group at R1 and hydrogen, unsubstituted phenyl or fluoro-substituted phenyl group at R2 is conducive to the MT1 binding affinity. The binding affinity decreases if furyl substitution at R3 position is present. The best equation describing MT2 binding affinity [n = 34, Q2 = 0.602, Ra2 = 0.755, R2 = 0.792, R = 0.890, s = 0.584, F = 213 (df 5, 28)] shows that the MT2 binding affinity depends on the similar factors as described for MT1 binding affinity; however, the contributions of the factors for the two affinities are different to some extent as evidenced from the regression coefficients. Among the selectivity relations, the best equation [n = 33, Q2 = 0.496 Ra2 = 0.681, R2 = 0.721, R = 0.849, s = 0.458, F = 18.1(df 4, 28)] suggests that MT2 binding increases with increase in value of n, presence of methoxy group at R1, and E-state values of different atoms of the benzofuran ring, while it decreases in presence of furyl group at R3 position.

Benzofurans↗
PubMed2004

QSAR modeling of globulin binding affinity of corticosteroids using AM1 calculations.

A quantitative structure-activity analysis of binding affinity of a series of 30 steroids for corticosteroid-binding globulin was performed using Wang-Ford charges of the non-hydrogen common atoms obtained from molecular electrostatic potential surface of AM1 optimized energy-minimized geometries of the compounds. Attempts were made to include lipophilicity (logP) and molar refractivity (MR) values of the whole molecules in the multivariate relations. The final relations were subjected to 'leave-one-out' cross-validation to check their predictive potential. It was found from the study that the charges of different atoms of the steroid nucleus [atoms 3, 4, 5 (ring A), 8, 9 (fusion points of rings B and C) and 16 (ring D)] contribute significantly to the binding affinity. This suggests the importance of these atoms/sites for the globulin binding affinity, which is also supported by previous reports on structure-activity relations of corticosteroids. Further, molar refractivity shows parabolic relation with the binding affinity, which indicates the possibility of dispersion interactions. The statistical qualities of the final equations generated in the present study (predicted variance 77-82%; explained variance 83-87%) are better than those of some of the previously reported models.

Adrenal Cortex Hormones↗
PubMed2004

QSAR modeling of HIV-1 reverse transcriptase inhibitor 2-amino-6-arylsulfonylbenzonitriles and congeners using molecular connectivity and E-state parameters.

Anti-HIV-1 activity (assayed in MT-4 cell line) and HIV-1 reverse transcriptase (RT) binding affinity of 2-amino-6-arylsulfonylbenzonitriles and their thio and sulfinyl congeners (Chan et al., J. Med. Chem., 2001, 44, 1866) have been modeled using E-state index along with molecular connectivity and indicator parameters in an attempt to explore the different fragments of the molecules contributing significantly to the activities. Stepwise multiple regression procedure was adopted to develop the topological models. The models generated were of acceptable statistical quality and predictive potential. The results show that for both the response variables, first order fragmental valence molecular connectivity of the meta substituents of the aryl ring plays a significant role: second meta substituents show supraadditive action on the activities probably due to enhanced binding (presumably through dispersion interaction) of the ligand with the binding site. Again, presence of sulfone moiety contributes significantly to the activities. Further, presence of meta-trifluoromethyl group at the aryl ring is detrimental for both the activity parameters. Additionally, the anti-HIV-1 model shows specific contributions of the E-state values of different atoms and positive contribution of the ortho-methoxy group present on the aryl ring.

Amination↗