Group-based QSAR Research Articles

Assessment of Structural Basis for Thiazolopyridine Derivatives as DNA Gyrase-B Inhibitors.

Tuberculosis (TB) is one of the leading causes of death in the post-COVID- 19 era. It has been observed that there is a devastating condition with a 25-30% increase in TB patients. DNA gyrase B isoform has proved its high potential to be a therapeutically effective target for developing newer and safer anti-TB agents. This study aims to identify minimum structural requirements for the optimization of thiazolopyridine derivatives having DNA gyrase inhibitory activities. Moreover, developed QSAR models could be used to design new thiazolopyridine derivatives and predict their DNA gyrase B inhibitory activity before synthesis. 3D-QSAR and Group-based QSAR (G-QSAR) methodologies were adopted to develop accurate, reliable, and predictive QSAR models. Statistical methods such as kNN-MFA SW-FB and MLR SW-FB were used to correlate dependent parameters with descriptors. Both models were thoroughly validated for internal and external predictive abilities. The 3D-QSAR model significantly correlated steric and electrostatic descriptors with q2 0.7491 and predicted r2 0.7792. The G-QSAR model showed that parameters such as SsOHE-index, slogP, ChiV5chain, and T_C_C_3 were crucial for optimizing thiazolopyridine derivatives as DNA gyrase inhibitors. The 3D-QSAR model was interpreted extensively with respect to 3D field points, and the pattern of fragmentation was studied in the G-QSAR model. The 3D-QSAR and G-QSAR models were found to be highly predictive. These models could be useful for designing potent DNA gyrase B inhibitors before their synthesis.

Rational in silico drug design of HIV-RT inhibitors through G-QSAR and molecular docking study of 4-arylthio and 4-aryloxy-3-iodopyridine-2(1-H)-one derivative

BackgroundHuman immunodeficiency virus infection and acquired immune deficiency syndrome (HIV/AIDS) is a spectrum of conditions caused by infection with the human immunodeficiency virus (HIV). Antiretroviral therapy (ART) against HIV infection offers the promise of controlling disease progression and prolonging the survival of HIV-infected patients. Reverse transcriptase (RT) inhibitors remain the cornerstone of the drug regimen to treat AIDS. In this direction, by using group-based QSAR study (G-QSAR), identification of the structural need for the development of lead structure with reverse transcriptase inhibition on 97 reported structures was carried out. Docking analysis was performed further and suggested the structural properties required for binding affinity with the receptor. The molecules in the data set were fragmented into six (R1, R2, R3, R4, R5, and R6) by applying the fragmentation pattern. Three G-QSAR models were selected based on the statistical significance of the model. The molecular docking study was performed to explain the structural properties required for the design of potent HIV-RT inhibitors.ResultsThe statistically validated QSAR models reveal the presence of higher hydrophobic groups containing single-bonded –Br atom, 2 aromatic bonded –NH group with less electronegativity, and entropic interaction fields at R2 essential for better anti-HIV activity. The presence of a lipophilic group at R3, oxygen and sulfur connected with two aromatic bonds at R4, and –CH3 group at R5 was fruitful for reverse transcriptase inhibition. Docking studies of the selected inhibitors with the active site of reverse transcriptase enzyme showed hydrogen bond, Van der Waal’s, charge, aromatic, and π–π interactions with residues present at the active site.ConclusionThe results of the generated models provide significant site-specific insight into the structural requirements for reverse transcriptase inhibition during the design and development of novel anti-HIV compounds. Molecular docking study revealed the binding interaction between the ligand and the receptor which gave insight towards the structure-based design for the discovery of more potent compounds with better activity against HIV infection.

Chemometric Modeling of Structurally Diverse Carbamates for the Inhibition of Acetylcholinesterase (AChE) Enzyme in Alzheimer's Disease

In this research, we have developed two-dimensional quantitative structure-activity relationship (2D-QSAR) and group-based QSAR (GQSAR) models employing a dataset of 78 carbamate derivatives (acetylcholinesterase enzyme inhibitors). The developed models were validated using various stringent validation parameters. From the insights obtained from the developed 2D-QSAR and GQSAR models, we have found that the structural features appearing in the models are responsible for the enhancement of the inhibitory activity against the AChE enzyme. Furthermore, we have performed the pharmacophore modeling to unveil the structural requirements for the inhibitory activity. Additionally, molecular docking studies were performed to understand the molecular interactions involved in binding, and the results are then correlated with the requisite structural features obtained from the QSAR and pharmacophore models.

Insight into structural requirements of antiamoebic flavonoids: 3D-QSAR and G-QSAR studies.

Plant-based flavonoids have been found to exhibit strong inhibitory capability against Entamoeba histolytica. So, various QSAR models have been developed to identify the critical features that are responsible for the potency of these molecules. 3D-QSAR analysis using k-nearest neighbour molecular field analysis via stepwise forward-backward variable selection method showed best results for both internal and external predictive ability of the model (i.e., q2 =0.64 and pred_r2 =0.56). Also, a group-based QSAR (G-QSAR) model was developed based on partial least squares regression combined with stepwise forward-backward variable selection method. It gave best parametric results (r2 =0.74, q2 =0.56 and pred_r2 =0.54) which implied that the model is highly predictive. 3D-QSAR established that presence/absence of bulk near rings B and C is important in deciding the inhibitory potential of these molecules. Additionally, G-QSAR provided site-specific clue wherein modifications related to molecular weight, electronegativity and separation of an oxygen atom in rings A and C can result in enhanced biological activity. To the best of the author's knowledge, this is the first QSAR study of antiamoebic flavonoids, and therefore, we expect the results to be useful in the design of more potent antiamoebic 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.

Computational identification of novel piperidine derivatives as potential HDM2 inhibitors designed by fragment-based QSAR, molecular docking and molecular dynamics simulations

Tumor suppressor protein p53 maintains integrity of genome and regulates the genes responsible for DNA repair mechanism, apoptosis as well as cell cycle and growth arrest. As with murine double minute 2 (MDM2), the human homolog HDM2 is a principal cellular antagonist of p53. In unstressed cells, cellular levels of p53 and HDM2 are maintained in an autoregulatory manner in which both mutually control cellular levels of each other. About half of the human cancers express wild-type p53 protein that is antagonized by over-expressed HDM2. Restoring p53 function via HDM2 antagonists is a leading therapeutic approach for treating a variety of tumors. In this study, we have developed a novel statistically sound group-based QSAR (GQSAR) model using piperidine-derived compounds that have been validated experimentally to inhibit p53–HDM2 interaction. On the basis of developed GQSAR model, a combinatorial library of molecules was prepared and its activity was predicted. These molecules were then docked to HDM2, and two top-scoring molecules possessing a binding energy of −6.639 and −6.305 kcal/mol were selected for further study. These molecules and their binding poses were analyzed further via molecular dynamic simulations. In this study, we report two lead compounds as potent HDM2 inhibitors and also provide an insight into mechanism of interaction of the lead compounds to HDM2 target.

First report on exploring classification and regression based QSAR modelling of Plasmodium falciparum glycogen synthase kinase (PfGSK-3) inhibitors

Recent advances in the knowledge of parasite biology have unveiled many new antimalarial targets for drug development. The glycogen synthase kinase-3 from Plasmodium falciparum (PfGSK-3) plays an active role in the completion of the asexual erythrocytic stage of P. falciparum life cycle. Due to the limited availability of experimental information (only one dataset is publicly available to our knowledge) and the absence of the target protein structure, the discovery of new inhibitors against PfGSK-3 is quite challenging. Against this background, we have made an effort to develop classification-based (using linear discriminant analysis or LDA) and regression-based quantitative structure-activity relationship (using group based-QSAR or G-QSAR) models for the categorization and quantitative prediction, respectively, of the activity of PfGSK-3 inhibitors. The classification model highlighted the contribution of electronic (Dipole-mag) and topological (S_tsC) descriptors in discriminating the PfGSK-3 inhibitors into more active and less active classes. The regression-based G-QSAR model showed the contribution of fragment-based descriptors (R1-chiV3 and R2-Most-vePotential) in determining the PfGSK-3 inhibitory activity, and also suggested modification sites for the improvement of PfGSK-3 inhibitory activity. The information obtained from this work could be utilized for the identification of novel PfGSK-3 inhibitors with a hope of overcoming the antimalarial resistance problem.

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.

A Structure–Activity Relationship Study of Naphthoquinone Derivatives as Antitubercular Agents Using Molecular Modeling Techniques

Tuberculosis (TB) is one of the major causes of death worldwide. Mycobacterium tuberculosis, the leading causative agent of TB, is responsible for the morbidity and mortality of a large population worldwide. In view of above and as a part of our effort to develop new and potent anti-TB agents, a series of substituted naphthoquinone derivatives were subjected to molecular modeling using various feature selection methods. The statistically significant best 2D-QSAR model having correlation coefficient [Formula: see text] and cross-validated squared correlation coefficient [Formula: see text] with external predictive ability of [Formula: see text] was developed by SA-PLS, and group-based QSAR model having [Formula: see text] and [Formula: see text] with [Formula: see text] was developed by SA-PLS. Further analysis using three-dimensional QSAR technique identifies a suitable model obtained by SA-partial least square method leading to antitubercular activity prediction. k-nearest neighbor molecular field analysis was used to construct the best 3D-QSAR model using SA-PLS method, showing good correlative and predictive capabilities in terms of [Formula: see text] and [Formula: see text]. The pharmacophore analysis results obtained from this study show that the distance between the aromatic/hydrophobic and the naphthoquinone moiety sites to the aliphatic and acceptor groups should be connected with almost the same distance for significant antitubercular activity. The information rendered by QSAR models may lead to a better understanding of structural requirements of antitubercular activity and also can help in the design of novel potent antitubercular activity.

QSAR studies on 3-(4-biphenylmethyl) 4, 5-dihydro-4-oxo-3H-imidazo [4, 5-c] Pyridine derivatives as angiotensin II (AT1) receptor antagonist

QSAR studies were performed for correlating the chemical composition of 3-(4-biphenylmethyl) 4, 5-dihydro-4-oxo-3H-imidazo [4, 5-c] pyridines bearing aryl acetic acid esters and acetamides as angiotensin II AT(1) receptor antagonist. Four different quantitative structure-property relationship (QSAR) methods namely two-dimensional (2D-QSAR), group-based QSAR, k-nearest neighbor and Pharmacophore Modeling were employed to obtain statistically significant models. The statistically significant best 2D-QSAR model having correlation coefficient r(2) = 0:8940 and cross-validated squared correlation coefficient q(2) = 0:7648 with external predictive ability of pred_r(2) = 0:8177,pred_r(2)se = 0.4119 and best group-based QSAR model having r(2) = 0:7392 and q(2) = 0:6710with pred_r(2) = 0:7503was developed by SA-principal component regression. The most predictive k-nearest neighbor model derived from the superposition of conformations has good cross-validated q(2) = 0:7637 and satisfied predictive ability r(2)_pred = 0.7143. Continuing with compounds of substituted 4, 5-dihydro-4-oxo-3H-imidazo [4, 5-c] pyridine derivatives chemical feature-based pharmacophore models with lowest RMSD value (0.3292 Å) consists of two Hac (Hydrogen bond acceptor), negative ionizable, and two AroC (Aromatic) features are important for the activity. The study suggested that substitution of group at R, R 1, R 2 and Ar, and position on 4, 5-dihydro-4-oxo-3H-imidazo [4, 5-c] pyridine ring with more electronegative nature and low bulkiness are favorable for the antihypertensive activity. These theoretical results may provide a useful reference for understanding the action mechanism and designing potential angiotensin II (AT1) receptor antagonist.

Exploring structural requirements of imaging agents against Aβ plaques in Alzheimer's disease: a QSAR approach.

Exploring molecular imaging agents against the beta amyloid (Aβ) plaques for an early detection of Alzheimer's disease (AD) is one of the emerging research areas in medicinal chemistry. In the present in-silico study, a congeneric series of 44 imaging agents, including 17 positron emission tomography (PET) and 27 single photon emission computed tomography (SPECT) imaging agents, was utilized to understand the structural features required for having essential binding affinity against Aβ plaques. Here, 2D-quantitative structure-activity relationship (2D-QSAR) and group-based QSAR (G-QSAR) models have been developed using genetic function approximation (GFA) and validated using various statistical metrics. Both the models showed satisfactory performance signifying the reliability and robustness of the developed QSAR models. The vital information gained from both the QSAR models will be useful in developing new PET and SPECT imaging agents and also in predicting their binding affinity against Aβ plaques. The results of this study would be important in view of the widespread clinical applicability of the SPECT imaging agents, especially in the developing countries. In this study, we have also designed some imaging agents based on the information provided by the models. Some of these designed compounds were predicted to be similar to or more active than the most active imaging agents present in the original dataset.

Computational design of novel flavonoid analogues as potential AChE inhibitors: analysis using group-based QSAR, molecular docking and molecular dynamics simulations

Acetyl cholinesterase (AChE) is an enzyme associated with the loss of cholinergic neurones in Alzheimer’s disease. Acetylcholine is an important neurotransmitter found in the brain and the levels of which decrease significantly in Alzheimer’s patients due to increased expression of AChE. In this study, a novel fragment-based QSAR model has been developed using twenty-seven flavonoid-derived compounds exhibiting inhibitory activity against AChE. This fragment-based method gives the advantage of studying the effect of individual fragments on the biological activity of the compound by evaluating the descriptors. The compounds were divided into training and test sets, where the test set was used for cross-validation of the model. The QSAR model exhibited good statistical values for the training set (r 2 = 0.8070, q 2 = 0.7088, F-ratio = 31.3616) and test set (pred_r 2 = 0.8131). The regression equation obtained had three descriptors describing effect of substitutions in terms of quantitative values. Evaluation of the model implied that electronegative substitution at R1 position lowers the inhibitory activity, while the presence of hydroxyl group improves the same. The presence of rings increased the activity of the compounds. The model thus generated was used to obtain six combinatorial libraries and predicts the activity of these compounds. These compounds were selected for docking and molecular dynamics simulation studies and two leads were identified against AChE.

Group-based QSAR and molecular dynamics mechanistic analysis revealing the mode of action of novel piperidinone derived protein–protein inhibitors of p53-MDM2

Tumour suppressor p53 is known to play a central role in prevention of tumour development, DNA repair, senescence and apoptosis which is in normal cells maintained by negative feedback regulator MDM2 (Murine Double Minute 2). In case of dysfunctioning of this regulatory loop, tumour development starts thus resulting in cancerous condition. Inhibition of p53-MDM2 binding would result in activation of the tumour suppressor. In this study, a novel robust fragment-based QSAR model has been developed for piperidinone derived compounds experimentally known to inhibit p53-MDM2 interaction. The QSAR model developed showed satisfactory statistical parameters for the experimentally reported dataset (r2=0.9415, q2=0.8958, pred_r2=0.8894 and F-test=112.7314), thus judging the robustness of the model. Low standard error values (r2_se=0.3003, q2_se=0.4009 and pred_r2_se=0.3315) confirmed the accuracy of the developed model. The regression equation obtained constituted three descriptors (R2-DeltaEpsilonA, R1-RotatableBondCount and R2-SssOCount), two of which had positive contribution while third showed negative correlation. Based on the developed QSAR model, a combinatorial library was generated and activities of the compounds were predicted. These compounds were docked with MDM2 and two top scoring compounds with binding affinities of −10.13 and −9.80kcal/mol were selected. The binding modes of actions of these complexes were analyzed using molecular dynamics simulations. Analysis of the developed fragment-based QSAR model revealed that addition of unsaturated electronegative groups at R2 site and groups with more rotatable bonds at R1 improved the inhibitory activity of these potent lead compounds. The detailed analysis carried out in this study provides a considerable basis for the design and development of novel piperidinone-based lead molecules against cancer and also provides mechanistic insights into their mode of actions.

Development of dual inhibitors against Alzheimer's disease using fragment-based QSAR and molecular docking.

Alzheimer's (AD) is the leading cause of dementia among elderly people. Considering the complex heterogeneous etiology of AD, there is an urgent need to develop multitargeted drugs for its suppression. β-amyloid cleavage enzyme (BACE-1) and acetylcholinesterase (AChE), being important for AD progression, have been considered as promising drug targets. In this study, a robust and highly predictive group-based QSAR (GQSAR) model has been developed based on the descriptors calculated for the fragments of 20 1,4-dihydropyridine (DHP) derivatives. A large combinatorial library of DHP analogues was created, the activity of each compound was predicted, and the top compounds were analyzed using refined molecular docking. A detailed interaction analysis was carried out for the top two compounds (EDC and FDC) which showed significant binding affinity for BACE-1 and AChE. This study paves way for consideration of these lead molecules as prospective drugs for the effective dual inhibition of BACE-1 and AChE. The GQSAR model provides site-specific clues about the molecules where certain modifications can result in increased biological activity. This information could be of high value for design and development of multifunctional drugs for combating AD.

A Neural Network-Based QSAR Approach for Exploration of Diverse Multi-Tyrosine Kinase Inhibitors and its Comparison with a Fragment- Based Approach

Receptor and non-receptor tyrosine kinases have emerged as clinically useful drug target for treating certain types of cancer. It is well known that tyrosine kinase inhibitors with multi-kinases inhibitory potency are useful in anticancer therapy. In recent study, we have demonstrated application of a novel Group based QSAR (GQSAR) method to assist in lead optimization of multi-tyrosine kinase (PDGFR-beta, FGFR-1 and SRC) inhibitors. Although GQSAR method provides an alternative way to design new compounds, it could not be applied for virtual screening of large databases, because of its limitation to fragment each of the compound in the diverse database. So to circumvent this limitation of GQSAR method, herein we present the development of multi-kinase QSAR model using artificial neural networks. Various simple, easy and fast to calculate 2D/3D descriptors were used in the present analysis. The resulting neural network based QSAR (NN-QSAR) model was found to be statistically significant and provided insight into common structural requirements to inhibit different tyrosine kinases. The NN-QSAR model suggests five descriptors viz. number of rotatable bonds, number of hydrogen bond donors, number of building blocks, polar surface area and sum of nitrogen and oxygen atoms to be of major importance in explaining the activity variation in all the three kinases. In addition, this multi-target QSAR model could be useful to predict the activities of new compounds designed as tyrosine kinase inhibitors.

Structural insights for substituted acyl sulfonamides and acyl sulfamides derivatives of imidazole as angiotensin II receptor antagonists using molecular modeling approach

Molecular modeling studies were carried out on a series of substituted acyl sulfonamides and acyl sulfamides derivatives of imidazole as angiotensin II AT1 receptor antagonists in order to determine the structural properties required for their antihypertensive activity. To elucidate the structural properties required for antihypertensive activity, four different molecular modeling techniques; two-dimensional (2D-QSAR), Group-Based QSAR (G-QSAR), 3D-QSAR and pharmacophore identification studies have been carried out on a series of substituted acyl sulfonamides and acyl sulfamides derivatives of imidazole. The partial least square (PLS) regression method and kNN-MFA methodology coupled with feature selection methods viz., stepwise (SW) 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 best 2D-QSAR model having r2=0.8711 and q2=0.8054 with pred_r2=0.8410 was developed by SW-PLS and best G-QSAR model having r2=0.8196 and q2=0.7299 with pred_r2=0.7693 was developed by SW-PLS method. The best 3D-QSAR model using SA-kNN-MFA method, showing good correlative and predictive capabilities in terms of q2=0.7862 and pred_r2=0.7119. The contour maps generated by the kNN models were used to identify the key structural requirements responsible for the biological activity. Continuing with substituted acyl sulfonamides and acyl sulfamides derivatives of imidazole, chemical feature based pharmacophore model with lowest RMSD value (0.0436Å), consists of one AroC feature (aromatic), two AlaC features (aliphatic), one HAc (hydrogen bond acceptor), and one negative ionizable was developed. It could rationalize the antihypertensive activity profile of many compounds used in such study as well as the important structural features responsible for antihypertensive activity. The findings of these molecular modeling studies can serve as a useful guide for future rational design of potential agents with better antihypertensive activity.

QSAR and pharmacophore approach on substituted imidazole derivatives as angiotensin II receptor antagonists

A QSAR analyses of 32 aminomethyl and acylaminomethyl substituents derivatives were carried out to interpret the relationship between structural properties and angiotensin II AT1 receptor activity. Two-dimensional (2D-QSAR), Group-based (G-QSAR), 3D-QSAR, and pharmacophore mapping studies were performed using partial least square and k-nearest neighbor methodology coupled with various feature selection methods, viz. stepwise, genetic algorithm, and simulated annealing (SA) to derive QSAR models which were further validated for statistical significance and predictive ability by internal and external validation. The activity contributions of whole compounds and their substituents were determined from regression equation. The statistically significant best 2D-QSAR model 1 having r 2 = 0.8754 and q 2 = 0.7231 with $$ {\text{pred}}\_r^{ 2} = \, 0. 8 3 8 9 $$ was developed by GA-PLS with the descriptors like SdssCcount, SssNHcount and SaaaCcount. The best Group-based QSAR model-5 having r 2 = 0.7516 and q 2 = 0.6714 with $$ {\text{pred}}\_r^{ 2} = \, 0. 7 3 0 9 $$ was developed by GA-PLS. The 2D-QSAR results agreed with those from the Group based QSAR model and suggested the structural features of SssOE-index, Mol.Wt and SssNHE-index functionality that would enhance the antihypertensive activity of the imidazole derivatives in this series. The results of two-dimensional, Group-based QSAR showed that a combination of topological indices, hydrophobic properties, and auto-correlation descriptors of different atomic properties could be explored to design potent antihypertensive agents. The k-nearest neighbor approach was used to generate three-dimensional quantitative structure–activity relationship (3D-QSAR) models for these sets of molecules. The present study is an attempt in this direction seeking for the development and comparison of QSAR models of substituted imidazoles by different feature selection methods, which ultimately establishes the superiority of the GA-based models. Statistically genetic algorithm (GA) selection k-nearest neighbor (GA-kNN-MFA) model with respect to both the internal (q 2 = 0.7856) as well as external (pred_r 2 = 0.8193) model validation and correctly predicts the activity of calculated 78.56 % and calculated 81.93 % for the training and test set, respectively. Continuing with the series of substituted imidazoles derivatives, chemical feature-based pharmacophore models with lowest RMSD value (0.1397 A), consisting of one aromatic carbon center, two hydrogen bond acceptor, and one hydrogen bond donor features, was developed. The information rendered by 2D-QSAR, Group-based QSAR, k-nearest neighbor, and pharmacophore identification models may lead to a better understanding of structural requirements of antihypertensive agents and can help in the design of novel potent molecules.

Comparative QSAR and pharmacophore modeling of substituted 2-[2′-(dimethylamino) ethyl]-1, 2-dihydro-3H-dibenz[de,h]isoquinoline-1,3-diones derivatives as anti-tumor activity

To elucidate the structural properties required for anti-tumor activity, four different molecular modeling techniques; two-dimensional (2D-QSAR), group-based QSAR (G-QSAR), 3D-QSAR, and pharmacophore identification studies have been carried out on a series of substituted 2-[2′-(dimethylamino) ethyl]-1, 2-dihydro-3H-dibenz[de,h]isoquinoline-1,3-diones derivatives. The partial least square (PLS) regression method and k-nearest neighbor -MFA methodology coupled with feature selection methods viz. genetic algorithm and simulated annealing were applied to derive QSAR models which were further validated for statistical significance and predictive ability by internal and external validation. Further analysis using two-dimensional, group-based QSAR and k-nearest neighbor QSAR technique identifies a suitable model obtained by genetic algorithm and simulated annealing coupled with partial least square method leading to anti-tumor activity prediction. The statistically significant best 2D-QSAR model having r2 = 0.9041 and q2 = 0.8357 with pred_r2 = 0.8633 and best group-based QSAR model having r2 = 0.7315 and q2 = 0.6743 with pred_r2 = 0.7950 was developed by Genetic algorithm (melanoma)-PLS method. Further analysis using three-dimensional QSAR technique identifies a suitable model obtained by GA-partial least square method leading to anti-tumor activity prediction. The conformational alignment provided by the above-best pharmacophore was found to be highly robust as proved by the additional kNN-MFA approach studies. The best Genetic algorithm (melanoma)-PLS model with good external and internal predictivity for the training and test set has shown cross validation (q2) and external validation (pred_r2) values of 0.78 and 0.85, respectively. The influences of steric, electrostatic, and hydrophobic field effects generated by the contribution plot are analyzed and discussed. Continuing with the series of substituted 2-[2′-(dimethylamino) ethyl] -1, 2-dihydro-3H-dibenz[de,h]isoquinoline-1,3-diones derivatives chemical feature-based pharmacophore models with lowest RMSD value (0.0861 A°), consists of one AroC (aromatic), two HAc (hydrogen-bond acceptor), and one negative ionizable features were developed. In addition to the 3D QSAR models, impact of two-dimensional and group-based (2D; thermodynamic, structural, and topological including E-state parameters) descriptors toward the inhibitory activity was also studied. The information rendered by 2D-QSAR, group-based QSAR, k-nearest neighbor and Pharmacophore identification models may lead to a better understanding of structural requirements of anti-tumor and can help in the design of novel potent molecules.