Group-based Quantitative Structure–activity Relationship Model Research Articles

Investigation of fragment based quantitative regression on a series of substituted chromen-2-one derivatives as FXa inhibitors

ABSTRACT Factor Xa (FXa), a trypsin-like serine protease, is well-established target for the development of the anticoagulants. Number of molecules were reported as Factor Xa inhibitors but most of them have pharmacokinetic issues. In this present communication, we report development and validation of the group based quantitative structure activity relationship (GQSAR) studies on 48 chromen-2-one derivatives as effective inhibitors of FXa. All the molecules were fragmented into eleven functional fragments (R1, R2, R3, R4, R5, R6, R7, R8, R9, R10 and R11). All the developed GQSAR models were generated using multiple linear regressions analysis (MLR). The generated GQSAR models were selected on the basis of statistical data that models having r 2 should be above 0.6 were used to check the external predictivity while the significance of the model was decided on the basis of F value. Developed GQSAR models reveled presence of lipophilic groups on fragment R6 will diminish the bio-activity while at R2 it will lead to increase in bioactivity of molecules. Additionally, minimum number of rotatable bonds at fragments R1 was fruitful for better FXa inhibition activity. The results of GQSAR models may lead to better understanding of design and development of novel FXa inhibitors.

Novel group-based QSAR and combinatorial design of CK-1δ inhibitors as neuroprotective agents.

BackgroundTar DNA binding protein 43 (TDP-43) hyperphosphorylation, caused by Casein kinase 1 (CK-1) protein isoforms, is associated with the onset and progression of Amyotrophic Lateral Sclerosis (ALS). Among the reported isoforms and splice variants of CK-1 protein superfamily, CK-1δ is known to phosphorylate different serine and threonine sites on TDP-43 protein in vitro and thus qualifies as a potential target for ALS treatment.ResultsThe developed GQSAR (group based quantitative structure activity relationship) model displayed satisfactory statistical parameters for the dataset of experimentally reported N-Benzothiazolyl-2-Phenyl Acetamide derivatives. A combinatorial library of molecules was also generated and the activities were predicted using the statistically sound GQSAR model. Compounds with higher predicted inhibitory activity were screened against CK-1δ that resulted in to the potential novel leads for CK-1δ inhibition.ConclusionsIn this study, a robust fragment based QSAR model was developed on a congeneric set of experimentally reported molecules and using combinatorial library approach, a series of molecules were generated from which we report two top scoring, CK-1δ inhibitors i.e., CHC (6-benzyl-2-cyclopropyl-4-{[(4-cyclopropyl-6-ethyl-1,3-benzothiazol-2-yl)carbamoyl]methyl}j-3-fluorophenyl hydrogen carbonate) and DHC (6-benzyl-4-{[(4-cyclopropyl-6-ethyl-1,3-benzothiazol-2-yl)carbamoyl]methyl}-2-(decahydronaphthalen-1-yl)-3-hydroxyphenyl hydrogen carbonate) with binding energy of −6.11 and −6.01 kcal/mol, respectively.

Application of group-based QSAR on 2-thioxo-4-thiazolidinone for development of potent anti-diabetic compounds

To identify the structural requirement for development of lead structures with PPAR gamma binding activity group based quantitative structure activity relationship (GQSAR) studies on 46 reported structures were carried out. The molecules in the current dataset were fragmented into seven functional groups fragments (R1, R2, R3, R4, R5, R6 and R7). GQSAR models were derived using multiple linear regressions analysis. Four generated GQSAR models were selected based on the statistical significance of the model. It was found that the presence of smaller groups on fragment R7 and presence of lipophilic group at fragment R2 was conducive for PPAR gamma binding. Additionally, the existence of hydrogen bond acceptor at fragments R6 was fruitful PPAR gamma binding. The generated models provide a site-specific insight into the structural requirements PPAR γ binding which can be used to design and develop potent antidiabetic compounds.

Discovery of Potent Antihypertensive Ligands Substituted Imidazolyl Biphenyl Sulfonylureas Analogs as Angiotensin II AT1 Receptor Antagonists by Molecular Modelling Studies.

To elucidate the structural properties required for antihypertensive activity, three different molecular modelling techniques, two-dimensional quantitative structure activity relationship (2D-QSAR), group-based quantitative structure activity relationship (G-QSAR), and three-dimensional quantitative structure activity relationship (3D-QSAR) studies, have been carried out on a series of substituted imidazolyl biphenyl sulfonylureas derivatives. Multiple linear regressions methodology, viz. variable simulated annealing and stepwise methods, was applied to derive models which were further validated for statistical significance and predictive ability by internal and external validation. The best 2D-QSAR model was selected, having showed best predictability of activity with cross-validated value [Formula: see text], coefficient of determination [Formula: see text]. The [Formula: see text]_pred value of 0.7651 indicates predictability of test set analogues and reveals a significant and robust model, and best G-QSAR model having [Formula: see text] and [Formula: see text] with pred_[Formula: see text] was developed by SA-MLR. Using k-nearest neighbour approach, various 3D-QSAR models were generated and selected on the basis of [Formula: see text] and predictive [Formula: see text] values. The analysed best 3D-QSAR model revealed a good fit, having [Formula: see text] value of 0.8240 and [Formula: see text] value of 0.7523. The predictive power of the model generated was validated using a test set comprising molecules with pred_[Formula: see text] value of 0.7299. The results of two-dimensional QSAR and group-based QSAR showed that a combination of revealed the key role of Baumann's alignment-independent topological descriptors along with other descriptors such as the number of SsssCH count, SaasCE-index, SsOHcount indices properties could be explored to design potent antihypertensive agents. Finally, it is hoped that the work presented here will play an important role in understanding the relationship of physiochemical parameters with structure and biological activity.

Application of Group-Based QSAR and Molecular Docking in the Design of Insulin-Like Growth Factor Antagonists

Purpose: To identify the structural requirements for designing a lead key for insulin-like growth factor (IGF-1R) inhibition using group-based quantitative structure activity relationship (GQSAR) and molecular docking.Methods: GQSAR method requires fragmentation of molecules. The molecules in the current dataset were fragmented into three (R1, R2 and R3) by applying common fragmentation pattern, and fragmentbased 2D descriptors were then calculated. GQSAR models were derived by applying various methods including multiple linear regressions and partial least square or k-nearest neighbour.Results: Four generated GQSAR models were selected based on the statistical significance of the model. It was found that the presence of flexible and non-aromatic groups on fragment R1 was conducive for inhibition. Additionally, the existence of amino groups as hydrogen bond donors at fragments R2 and R3 was fruitful for inhibition. Docking studies revealed the binding orientation adopted by the active compounds at several amino acid residues, including Met 1126, Arg, 1128, Met 1052, GLU 1050, Met 1112, Leu 1051, Met 1049, Val 1033, and Val 983 at ATP binding sites of IGF-1R kinase domain.Conclusion: The generated models provide a site-specific insight into the structural requirements for IGF-1R inhibition which can be used to design and develop potent inhibitors.Keywords: Insulin-like growth factor 1 (IGF-1) receptor, Quantitative structure-activity relationship, Adenosine triphosphate, Competitive inhibitors, Electrotopological state index.

Discovery of potent antihypertensive ligands substituted imidazolyl biphenyl sulfonylureas analogs as angiotensin II AT1 receptor antagonists by molecular modelling studies.

To elucidate the structural properties required for antihypertensive activity, three different molecular modeling techniques; two-dimensional Quantitative structure activity relationship (2D-QSAR), Group-Based Quantitative structure activity relationship (G-QSAR), and three-dimensional Quantitative structure activity relationship (3D-QSAR) studies have been carried out on a series of substituted imidazolyl biphenyl sulfonylureas derivatives. Multiple linear regressions methodology, viz. variable simulated annealing (SA) and stepwise (SW) methods, was applied to derive models which were further validated for statistical significance and predictive ability by internal and external validation. The best 2D-QSAR model was selected, having showed best predictability of activity with cross validated value (q2) = 0.7866, coefficient of determination (r2) =0.8003. The r2_pred value of 0.7651 indicates predictability of test set analogues and reveals a significant and robust model and best G-QSAR model having r2 = 0.7459 and q2 = 0.6712 with pred_r2 = 0.7105 was developed by SA -MLR. Using k-nearest neighbour (kNN) approach, various 3D QSAR models were generated and selected on the basis of q2 and predictive r2 values. The analyzed best 3D-QSAR model revealed a good fit, having r2 value of 0.8240 and q2 value of 0.7523. The predictive power of the model generated was validated using a test set comprising molecules with pred_r2 value of 0.7299. The results of two-dimensional QSAR, Group based QSAR showed that a combination of revealed the key role of Baumann's alignment independent topological descriptors along with other descriptors such as the number of hydrogen bond acceptors, hydrogen bond donors, rotatable bonds indices properties and auto-correlation descriptors of different atomic properties could be explored to design potent antihypertensive agents. Finally, it is hoped that the work presented here will play an important role in understanding the relationship of physiochemical parameters with structure and biological activity.

Novel Insight into the Structural Requirements of P70S6K Inhibition Using Group-based Quantitative Structure Activity Relationship (GQSAR)

Dysregulation of P70 ribosomal S6 kinase (P70S6K) has been observed in many cancers; therefore, the design of new molecules targeting p70S6K of paramount importance in cancer therapy. The current study employed a group-based quantitative structure-activity relationship (GQSAR) to develop global QSAR models capable of predicting the bioactivity of P70S6K inhibitors. A wide variety of chemical structures and biological activities (half maximal inhibitory concentration) of P70S6K inhibitors were collected from the binding database website. Compounds were classified into various chemical groups and then fragmented into R1, R2, and R3 fragments based on certain pharmacophoric features required for ligand-target biointeractions. Different two-dimensional fragment-based descriptors were calculated for each fragment. The dataset was then divided into a training set (n=40) and a test set (n=10) using a sphere exclusion algorithm. Multiple linear regressions coupled with simulated annealing or stepwise regression resulted in model A (r2=0.92) and model B (r2=0.87), respectively. Leave-one-out validation showed that models A and B have internal predictive abilities of 72% and 61%, respectively. External validation indicated that both models are robust, with squared cross-correlation coefficients of the training set (pred-r2) of 0.87 and 0.89, respectively. The developed GQSAR models indicate that fragment R3 plays a key role in activity variation (65%) with sound contribution of five-membered rings (5 chain count), aromatic carbons (SaaaCE-index), and aromatic nitrogens (SaaNcount). In contrast, fragments R1 and R2 together contribute 35% of activity variation, suggesting that sulfur atoms (Sulfur count) and hydrophobic threemembered rings (chi3 chain) at R1 are preferable for inhibitory activity.

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.

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.

Exploration of Important Sites of Antimalarial Endochins for Optimum Structural Modification Using Group-Based QSAR (G-QSAR) Modeling

The emergence of multidrug resistance of the currently available antimalarial drugs has led to the need of the discovery and development of new antimalarial compounds. In the present study, we have used a novel group based quantitative structure-activity relationship (G-QSAR) approach, which allows to establish a correlation of chemical group variation at different molecular sites of interest with the biological activity, using a series of 53 antimalarial endochin analogs. In our previous work, we developed QSAR models for this data set using different chemometric tools and tried to emphasize on importance of descriptor thinning and noise reduction prior to feature selection step. In the present paper, we have tried to select optimal subset of variables using a new strategy for the development of robust G-QSAR models. Starting with an initial pool of 6395 descriptors, we have finally used 51 descriptors for model development using genetic methods. The best model showed encouraging values for internal.....

Predictive Chemometric Modeling of DPPH Free Radical-Scavenging Activity of Azole Derivatives Using 2D- and 3D-Quantitative Structure–Activity Relationship Tools

The endogenous antioxidants often fail to manage the systemic free radical overload resulting from extensive exposure to environmental pollutants and improper diet. Such free-radical burden over a prolonged period leads to oxidative stress, which in turn, promotes an array of fatal diseases. Five different in silico methodologies have been employed here for a series of azole derivatives, which identify the essential structural attributes of the molecules and quantify the contributions of the prime molecular prerequisites for designing compounds with improved antioxidant activity. The importance of the different constituents is quantitatively analyzed using the descriptor-based quantitative structure-activity relationship and group-based quantitative structure-activity relationship models while the pharmacophore, comparative molecular similarity index analysis and hologram quantitative structure-activity relationship models serve as essential query tools for screening of azole compounds in order to select potent antioxidant molecules.