Group Based Quantitative Structure Activity Relationship 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.

Pharmacophore-similarity-based QSAR (PS-QSAR) for group-specific biological activity predictions

Recent technological breakthroughs in medicinal chemistry arena had ameliorated the perspectives of quantitative structure–activity relationship (QSAR) methods. In this direction, we developed a group-based QSAR method based on pharmacophore-similarity concept which takes into account the 2D topological pharmacophoric descriptors and predicts the group-specific biological activities. This activity prediction may assist the contribution of certain pharmacophore features encoded by respective fragments toward activity improvement and/or detrimental effects. We termed this method as pharmacophore-similarity-based QSAR (PS-QSAR) and studied the activity contribution of fragments from 3-hydroxypyridinones derivatives possessing antimalarial activities.

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.

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.....

Quantification of contributions of different molecular fragments for antioxidant activity of coumarin derivatives based on QSAR analyses

Attempts have been made in the present work using in silico techniques for identification of essential structural features imparting antioxidant potential to naturally available coumarin molecules and their synthetic derivatives. Four different types of modeling tools have been employed for the qualitative and quantitative assessment of the molecular fragments constituting the biological pharmacophore. The descriptor-based quantitative structure–activity relationship (QSAR) and group-based QSAR (G-QSAR) models provide a quantitative estimation of the substituent requirements and the chemical nature of the parent moiety. Subsequently, 3D pharmacophore and hologram QSAR (HQSAR) models enable identification of the key molecular components necessary for the antioxidant potency to the molecules. All of the different models infer the importance of the hydrogen bond acceptor ketonic fragment for interaction of the antioxidant molecules with the neighbouring toxic radicals. Additionally, the phenyl substituent attached to the side chain and the benzene nucleus of the benzopyran moiety also constitute the response pharmacophore for the molecules under study. The models thus developed may serve as an essential query tool for screening of databases for selection of molecules bearing the essential fragments and subsequent prediction of their free radical scavenging potency.

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.

Computational Approaches to Improve Aggrecanase-1 Inhibitory Activity of (4-keto) Phenoxy) Methyl Biphenyl-4-sulfonamide: Group Based QSAR and Docking Studies

Group based Quantitative Structure Activity Relationship (GQSAR) was developed for thirty (4-keto-phenoxy) methyl biphenyl-4-sulfonamides which exhibit aggrecanase-1 enzyme inhibitory activity. This enzyme is involved in osteoarthritis. The data is divided into training and test sets, where the latter is used for validating the model. Substitution in the R(1) position plays a major role when compared to substitution in R(2) position. The former position is influenced by two descriptors, namely electrotopological and connectivity indices. R(2) position is influenced by radius of gyration. The statistical parameters for the training set (r(2) = 0.80, r(2)adj = 0.77, q(2) = 0.69, F-ratio = 26.80 and standard error = 0.24) and the predicted r(2) (r(2)(test) =0.95) are satisfactory. Docking of the compounds with aggrecanase-1 enzyme showed that there is a strong negative correlation between the binding energy and aggrecanase-1 inhibitory activity. Compounds with the carbonyl substitution interact with the S'1 pocket which is needed for enhanced activity. The two methodologies described here can help in lead optimization.

Molecular modeling studies of some substituted chalcone derivatives as cysteine protease inhibitors

The quantitative structure–activity relationship (QSAR) studies were performed on a series of 42 chalcone derivatives to find out the structural requirements of their antimalarial activities. The multiple linear regression (MLR) and partial least square (PLS) regression methods coupled with various feature selection methods, viz., stepwise (SW), genetic algorithm (GA) and simulated annealing (SA) were applied to derive QSAR models which were further validated for statistical significance and predictive ability by internal and external validation. The statistically significant 2D-QSAR model having r 2 = 0.8892 and q 2 = 0.7508 with pred_r 2 = 0.7403 was developed by SW-MLR and best Group-based QSAR (GQSAR) model having r 2 = 0.7884 and q 2 = 0.7038 with pred_r 2 = 0.7339 was developed by SW-PLS. Molecular field analysis was used to construct the best 3D-QSAR model using k-nearest neighbour method, showing good correlative and predictive capabilities in terms of q 2 = 0.6818 and pred_r 2 = 0.7708. A docking study revealed the binding orientations of these inhibitors at active site amino acid residues (Gln36, Cys39, Lys37, Asp35, Trp206) of falcipain-2 enzyme (PDB ID: 3BPF). Both QSAR and docking studies of such derivatives provide guidance for further lead optimization and designing of more potent antimalarial agents.