Quantitative Structure-activity Relationship Studies Research Articles

Carbazole derivatives containing chalcone analogues targeting topoisomerase II inhibition: First principles characterization and QSAR modelling.

Recently, a series of carbazole derivatives containing chalcone analogues (CDCAs) were synthetized as potent anticancer agents and apoptosis inducers. These compounds target the inhibition of topoisomerase II and present cytotoxic activities. After comparison to experiment, we validated the use of B3LYP, a density functional theory-based approach, to describe the structure and molecular properties of the carbazole subunit and CDCAs compounds of interest. Then, we derived relationships between the chemical descriptors and activity of these carbazole derivatives using multi-parameter optimization and quantitative structure activity relationships (QSAR) approaches. For the QSAR studies, we used multiple linear regression and artificial neural network statistical modelling. Our predicted activities are in good agreement with the experimental ones. We found that the most important parameter influencing the activity of the considered compounds is the octanol-water partition coefficient, highlighting the importance of flexibility as a key molecular parameter to favor cell membrane crossing and enhance the action of these CDCAs against topoisomerase II. Our results provide useful guidelines for designing new oral active CDCAs medicaments for cytotoxic inhibition.

Lipoxygenase Inhibition Activity of Coumarin Derivatives-QSAR and Molecular Docking Study.

Lipoxygenases (LOXs) are a family of enzymes found in plants, mammals, and microorganisms. In animals and plants, the enzyme has the capability for the peroxidation of unsaturated fatty acids. Although LOXs participate in the plant defense system, the enzyme’s metabolites can have numerous negative effects on human health. Therefore, many types of research are searching for compounds that can inhibit LOXs. The best quantitative structure–activity relationship (QSAR) model was obtained using a Genetic Algorithm (GA). Molecular docking was performed with iGEMDOCK. The inhibition of lipoxygenase was in the range of 7.1 to 96.6%, and the inhibition of lipid peroxidation was 7.0–91.0%. Among the synthesized compounds, the strongest inhibitor of soybean LOX-3 (96.6%) was found to be 3-benzoyl-7-(benzyloxy)-2H-chromen-2-one. A lipid peroxidation inhibition of 91.0% was achieved with ethyl 7-methoxy-2-oxo-2H-chromene-3-carboxylate. The docking scores for the soybean LOX-3 and human 5-LOX also indicated that this compound has the best affinity for these LOX enzymes. The best multiple linear QSAR model contains the atom-centered fragment descriptors C-06, RDF035p, and HATS8p. QSAR and molecular docking studies elucidated the structural features important for the enhanced inhibitory activity of the most active compounds, such as the presence of the benzoyl ring at the 3-position of coumarin’s core. Compounds with benzoyl substituents are promising candidates as potent lipoxygenase inhibitors.

Three-dimensional Quantitative Structure-activity Relationship (3DQSAR) and Molecular Docking Study of 2-((pyridin-3-yloxy)methyl) Piperazines as α7 Nicotinic Acetylcholine Receptor Modulators for the Treatment of Inflammatory Disorders.

Comparative molecular field analysis (CoMFA) of 27 analogues of 2-((pyridin-3-yloxy)methyl)piperazine derivatives was carried out using software Tripos SYBYL X. Optimal r2 (0.854) and q2 (0.541) values were obtained for the developed 3D-QSAR model. The contour plots obtained from CoMFA analysis have shown 13.84% steric contribution and 66.14% electrostatic contribution towards an anti-inflammatory activity. The homology model of the receptor protein, α7 nicotinic acetylcholine, was generated in SWISS MODELLER using auto template mode and was analysed for the quality using Procheck, QMEAN Z-score, Anolea and GROMOS plots. The QMEAN score for the model was observed to be - 3.862. The generated model of alpha 7 nicotinic acetylcholine receptor was used for docking study of 27 piperazine analogues using Auto-Dock 4.2.5.1. The dock score obtained from docking analysis was then correlated with experimental pIC50 values for in-silico validation of the developed CoMFA model and a good correlation was obtained with correlation coefficient (r2) value of -0.7378. The present investigation suggests an optimal 3D-QSAR with CoMFA model for further evaluating new chemical entities based on piperazine skeleton.

Virtual Screening, Molecular Docking and QSAR Studies in Drug Discovery and Development Programme

Structure-based drug design (SBDD) and ligand-based drug design (LBDD) are the two basic approaches of computer-aided drug design (CADD) used in modern drug discovery and development programme. Virtual screening (or in silico screening) has been used in drug discovery program as a complementary tool to high throughput screening (HTS) to identify bioactive compounds. It is a preliminary tool of CADD that has gained considerable interest in the pharmaceutical research as a productive and cost-effective technology in search for novel molecules of medicinal interest. Docking is also used for virtual screening of new ligands on the basis of biological structures for identification of hits and generation of leads or optimization (potency/ property) of leads in drug discovery program. Hence, docking is approach of SBDD which plays an important role in rational designing of new drug molecules. Quantitative structure-activity relationship (QSAR) is an important chemometric tool in computational drug design. It is a common practice of LBDD. The study of QSAR gives information related to structural features and/or physicochemical properties of structurally similar molecules to their biological activity. In this paper, a comprehensive review on several computational tools of SBDD and LBDD such as virtual screening, molecular docking and QSAR methods of and their applications in the drug discovery and development programme have been summarized.
 Keywords: Virtual screening, Molecular docking, QSAR, Drug discovery, Lead molecule

Quantitative Structure-Activity Relationship Studies Of Series Of Chalcones Derivatives as Inhibitors Of Tumor Necrosis Factor-Alpha

Quantitative structure-activity relationship (QSAR) teqnique was used to predict the biological activity of a series of chalcones compounds as anti-inflammatory. 26 physicochemical descriptors are tested in QSAR equations configuration to predict biological effectiveness of compounds under study. The values of R2 in Eqs (1–3) ranged from 0.794–0.873, the F values ranged from 14.161–26.206 and the S values ranged from 0.262–0.334. The results demonstrated excellent models based on Eq.3, along with high of R2, F and minimum S by employing three parameters r(C3-C5), (LUMO+1) and (LUMO+2). This signifies that these parameters play a significant role in determining anti-inflammatory characteristics.

Quantitative structure-activity relationship model for classifying the diverse series of antifungal agents using ratio weighted penalized logistic regression

ABSTRACT One of the most challenging issues when facing a Quantitative structure-activity relationship (QSAR) classification model is to deal with the descriptor selection. Penalized methods have been adapted and have gained popularity as a key for simultaneously performing descriptor selection and QSAR classification model estimation. However, penalized methods have drawbacks such as having biases and inconsistencies that make they lack the oracle properties. This paper proposes an adaptive penalized logistic regression (APLR) to overcome these drawbacks. This is done by employing a ratio (BWR) of the descriptors between-groups sum of squares (BSS) to the within-groups sum of squares (WSS) for each descriptor as a weight inside the L1-norm. The proposed method was applied to one dataset that consists of a diverse series of antimicrobial agents with their respective bioactivities against Candida albicans. By experimental study, it has been shown that the proposed method (APLR) was more efficient in the selection of descriptors and classification accuracy than the other competitive methods that could be used in developing QSAR classification models. Another dataset was also successfully experienced. Therefore, it can be concluded that the APLR method had significant impact on QSAR analysis and studies.

STRUCTURAL REQUIREMENTS OF SOME TRIAZOLONE DERIVATIVES USING TOPOLOGICAL AND PHYSICOCHEMICAL DESCRIPTORS: QSAR APPROACH

Quantitative structure-activity relationship (QSAR) studies were performed on a series of triazolone analogs to find the structural requirements activity by two-dimensional studies. The statistically significant best 2D QSAR model derived from partial least square analysis is correlated with some of the parameters, viz. correlation coefficient (r2)with external ability of predictive activity. The results of this study may be useful to medicinal chemists to design more antihypertensive compounds.

Corrosion Inhibition Study on (Z)-N’-(1-phenylethylidene) benzohydrazide Derivatives on Carbon Steel in Acidic Solutions.

Decomposition of carbon steel via corrosion process has caused a serious damage to several organisations worldwide. Despite efforts put in place to curb this menace by scientists, havoc caused by corrosion of metal still remain. Therefore, in a way to find effective corrosion inhibitor, (Z)-N'-(1-phenylethylidene) benzohydrazide derivatives was optimised using density functional theory and the obtained molecular descriptors correlated well with the experimental percentage inhibition efficiency. Also, the developed quantitative structure-activity relationship study (QSAR) model revealed that dipole moment impeccably define anti-corrosion properties of (Z)-N'-(1-phenylethylidene) benzohydrazide Derivatives.

QSAR and Molecular Docking Studies of novel thiophene, pyrimidine, coumarin, pyrazole and pyridine derivatives as Potential Anti-Breast Cancer Agent

Quantitative Structure Activity Relationship (QSAR) and molecular Docking studies were carried out on some novel compounds to generate a good QSAR models that relate the anti-breast cancer activity values with the molecular structure of the compounds. Genetic Function Algorithm (GFA) and Multiple Linear Regression Analysis (MLRA) were used to select the descriptors that were used to build the models. The best model built was found to have statistical validation values of squared correlation coefficient (R2) = 0.999, adjusted squared correlation coefficient ( = 0.998, cross validation coefficient = 0.998 and an external squared correlation coefficient = 0.879 which was used to confirm the validation of the model. The docking results showed that ligands 6 and 5 with binding energy (-9.2kcalmol-1 and -9.0kcalmol-1) respectively have the highest binding affinity when compared to the reference drug doxorubicin with binding energy (-6.8kcalmol-1). The stability and robustness of the built model showed that new anti-breast cancer agents can be design from these derivatives.

Quantitative Structure-Activity Relationships of 1.2.3 Triazole Derivatives as Aromatase Inhibition Activity

Aromatase is an estrogen biosynthesis enzyme belonging to the cytochrome P450 family that catalyzes the rate-limiting step of converting androgens to estrogens. As it is pertinent toward tumor cell growth promotion aromatase is a lucrative therapeutic target for breast cancer. In the pursuit of robust aromatase inhibitors, a set of thirty 1-substituted mono- and bis-benzonitrile or phenyl analogs of 1.2.3-triazole letrozole were employed in quantitative structure activity relationship (QSAR) study using multiple linear regression (MLR).The results demonstrated good predictive ability for the MLR model. After dividing the dataset into training and test set. The models were statistically robust internally (R2 = 0.982) and the model predictability was tested by several parameters, including the external criteria (R2pred = 0.851. CCC= 0.946). Insights gained from the present study are anticipated to provide pertinent information contributing to the origins of aromatase inhibitory activity and therefore aid in our on-going quest for aromatase inhibitors with robust properties.

Potential Difference Driving Electron Transfer via Defective Carbon Nanotubes toward Selective Oxidation of Organic Micropollutants.

Nanocarbon-based persulfate oxidation emerges as a promising technology for the elimination of organic micropollutants (OMPs). However, the nature of the active site and its working mechanism remain elusive, impeding developments of high-performance oxidative technology for water treatment practice. Here, we report that defect-rich carbon nanotubes (CNTs) exhibit a superior activity in the activation of peroxymonosulfate (PMS) for OMP oxidation. Quantitative structure-activity relationship studies combined with theoretical calculations unveil that the double-vacancy defect on CNTs may be the intrinsic active site, which works as a conductive bridge to facilitate the potential difference-dominated electron transfer from the highest occupied molecular orbital of OMPs to the lowest unoccupied molecular orbital of PMS. Based on this unique mechanism, the established CNTs@PMS oxidative system achieves outstanding selectivity and realizes the target-oriented elimination of specific OMPs in a complicated aquatic environment. This work sheds new light on the mechanism of carbocatalysis for selective oxidation and develops an innovative technology toward remediation of practical wastewater.

Molecular docking, linear and nonlinear QSAR studies on factor Xa inhibitors

Factor Xa (FXa) enzyme has an important role in the blood coagulation system. Disruption in the enzyme function results in the production of blood clots. Therefore, inhibition of the factor Xa with anticoagulant drugs is an important target in thromboembolic therapy. Experimental design of new drugs is a time-consuming and expensive process. Application of molecular modeling is an essential step for designing new and high yield drugs to achieve better results. In this study, 36 aroylguanidine derivatives that inhibit destructive activities by binding to the FXa enzyme were selected. Quantitative Structure-Activity Relationships (QSAR) model was developed for predicting the IC50 parameter for factor Xa inhibitors. QSAR have been constructed by combining Genetic Algorithms with Multiple Linear Regressions (GA-MLR) and Support Vector Machine (SVM). The correlation coefficient (R2) and root mean square error (RMSE) for GA-MLR are 0.895, 0.142 and for SVM are 0.994, 0.032, respectively. The obtained results indicated that the SVM method is superior over the GA-MLR method. Also, molecular docking was used to examine the results more accurately, in which energy interactions were investigated. Molecular docking indicated the binding relationship between the receptor and the ligand. The results showed that reducing the parameters such as electronegativity, and the size of the atoms, as well as increasing the number of loops and groups attached to the structures, is effective in the model.

Computational Studies of bis-2-Oxoindoline Succinohydrazides and their In Vitro Cytotoxicity.

The discovery of clinically relevant EGFR inhibitors for cancer therapy has proven to be a challenging task. To identify novel and potent EGFR inhibitors, the quantitative structure-activity relationship (QSAR) and molecular docking approach became a very useful and largely widespread technique for drug design. We performed the in vitro cytotoxic activity on HEPG-2 cell line and earlier on MCF-7 and A 549 by using MTT assay method. The development of 3D QSAR model of N1,N4-bis(2-oxoindolin-3- ylidene) succinohydrazides using the stepwise-backward variable methods to generate Multiple Linear Regression method elucidates the structural properties required for EGFR inhibitory activity and also perform the Molecular Docking studies on EGFR (PDB ID:1M17). Further, we analysed for Lipinski's rule of five to evaluate the drug-likeness and established in silico ADMET properties. The resulting cytotoxicity (IC50) values ranged from 9.34 to 100 μM and compared with cisplatin as a standard. Among the series of compounds, 6j showed good cytotoxic activity on HEPG-2 cell line with 9.34 μM, IC50 value. Most of the evaluated compounds showed good antitumor activity on HEPG-2 than MCF-7and A549. The developed 3D QSAR Multiple Linear Regression models are statistically significant with non-cross-validated correlation coefficient r2 = 0.9977, cross-validated correlation coefficient q2 = 0.902 and predicted_r2 = 0.9205. Molecular docking studies on EGFR (PDB ID: 1M17) results, compounds 6d, 6j and 6l showed good dock/PLP scores i.e. -81.28, -73.98 and -75.37, respectively, by interacting with Leu-694, Val-702 and Gly-772 amino acids via hydrophobic and hydrogen bonds with Asn818 and Met- 769. Further, we analysed drug-likeness and established in silico ADMET properties. The results of 3D QSAR studies suggest that the electrostatic and steric descriptors influence the cytotoxic activity of succinohydrazides. From the molecular docking studies, it is evident that hydrophobic, hydrogen and Van Der Waal's interactions determine binding affinities. In addition to this, druglikeness and ADMET properties were analysed. It is evident that there is a correlation between the QSAR and docking results. Compound 6j was found to be too lipophilic due to its dihalo substitution on isatin nucleus, and can act as a lead molecule for further and useful future development of new EGFR Inhibitors.

General Fifth M-Zagreb Polynomials of Benzene Ring Implanted in the P-Type-Surface in 2D Network

Constitutional formulae of molecules are molecular graphs consisting of atoms as vertices and bonds between them represented as edges. The various physical, chemical, and biological properties of molecules are dependent on their molecular structures. The molecular structure is most important, not only to chemists but also to all scientists. The molecular structure descriptors or topological indices of molecules are a mathematical number or a set of selected invariants of matrices that are used to Quantitative Structure-Activity (-Property) Relationships (QSAR/QSPR) studies. In this paper, we computed some new degree-based topological indices of benzene ring implanted in the P-type-surface in the 2D network and its line subdivision of graph.

2D QSAR studies on a series of (4S,5R)-5-[3,5-bis(trifluoromethyl)phenyl]-4-methyl-1,3-oxazolidin-2-one as CETP inhibitors

ABSTRACT Cardiovascular disease (CVD) is one of the major causes of human death. Preliminary evidence indicates that the inhibition treatment of Cholesteryl Ester Transfer Protein (CETP) causes the most pronounced increase in HDL cholesterol reported so far. Merck has disclosed certain (4S,5R)-5-[3,5-bis(trifluoromethyl)phenyl]−4-methyl-1,3-oxazolidin-2-one derivatives, which show potent CETP inhibitory activity. Therefore, it would be desirable to develop computational models to facilitate the screening of these inhibitors. In the present work, quantitative structure–activity relationship (QSAR) models have been developed to predict the therapeutic potency of 108 derivatives of (4S,5R)-5-[3,5-bis(trifluoromethyl)phenyl]−4-methyl-1,3-oxazolidin-2-one: Multiple Linear Regression (MLR), Support Vector Regression (SVR) and Feedforward Neural Network using Particle Swarm Optimization (FNN-PSO). Six descriptors were selected using genetic algorithms, whereas, internal and external validation of the models was performed according to all available validation strategies. It was shown that CETP inhibitory activity is mainly governed by electronegativity, the structure of the molecule, and the electronic properties. The best results were obtained with the SVR model. The results obtained may assist in the design of new CETP inhibitors.

Docking and 3D-QSAR Studies of Hydrazone and Triazole Derivatives for Selective Inhibition of GRK2 over ROCK2

Introduction: G protein-coupled receptor kinase 2 (GRK2) is known to be implicated in heart failure, and therefore serves as an important drug target. GRK2 belongs to the protein kinase A, G, and C family and shares high sequence similarity with its closely related protein, the Rhoassociated coiled-coil protein kinase 2 (ROCK2). Therefore, selective inhibition of GRK2 over ROCK2 is considered crucial for heart failure therapy. Objective: To understand the structural factors for enhancing the inhibitory activity for GRK2 and selectivity over ROCK2, we analyzed and compared molecular interactions using the same set of ligands against both receptors. Methods: We have performed molecular docking and three-dimensional quantitative structure activity relationship (3D-QSAR) studies on a series of hydrazone and triazole derivatives. Results: The presence of hydrophobic substituents at the triazole ring, electronegative substituents between the pyridine and triazole ring and hydrophobic substituents near the benzene ring increases the activity of both kinases. Whereas, having non-bulky substituents near the triazole ring, bulky and hydrophobic substations at the benzene ring and electronegative and H-bond acceptor substituents at the triazole ring showed a higher inhibitory preference for GRK2 over ROCK2. Conclusion: The outcome of this study may be used in the future development of potent GRK2 inhibitors having ROCK2 selectivity.

Using Topomer Comparative Molecular Field Analysis to Elucidate Activity Differences of Aminomethylenethiophene Derivatives as Lysyl Oxidase Inhibitors: Implications for Rational Design of Antimetastatic Agents for Cancer Therapy

Topomer comparative molecular field analysis (topomer CoMFA) is applied to the quantitative structure-activity relationship (QSAR) study of aminomethylenethiophene (AMT) derivatives as lysyl oxidase (LOX) inhibitors. A total of thirty-six AMT derivatives were selected to build the QSAR model. The established topomer CoMFA model has the non-cross-validated correlation coefficient (r2) of 0.912 and the leave-one-out correlation coefficient (q2) of 0.540, which is statistically significant. The theoretically predicted anti-LOX potency agrees well with the experimentally observed inhibitory activity, proving the reasonable predictive ability of the QSAR model. The effect of molecular field information on the LOX inhibition of substituted aminomethylenethiophene was discussed in detail. The structural modification of the aminomethylenethiophene scaffold was carried out, and novel AMT derivatives with theoretically decent LOX inhibition were proposed. The topomer CoMFA modeling could provide a quantitative perspective into the structure-activity relationship of AMT derivatives and potentially speed up the rational design of LOX inhibitors as antimetastatic agents for cancer therapy.

Quantitative structure-activity relationship study for prediction of antifungal properties of phenolic compounds

Antifungal compounds are of interest to reduce commodity spoilage and exposure to mycotoxins. In this study, a series of quantitative structure-activity relationship (QSAR) equations based on topological properties were developed to gain insight into the antifungal activities of phenolic compounds. The molecules were geometry optimized using B3LYP/6-311+G** density functional theory calculations. Analysis of the frontier orbital properties revealed that conjugated phenolic compounds possessed smaller band gap energies. Genetic function approximation (GFA) on populations of 100 one to two descriptor models over 10,000 generations identified several models for antifungal activity against Fusarium verticillioides, Fusarium oxysporum, Aspergillus flavus, Aspergillus fumigatus, Penicillium expansum, and Penicillium brevicompactum. Phenolic compounds with greater antifungal activity possessed a lower electrophilicity index. The correlation coefficients for the one and two descriptor models ranged from 0.627 to 0.790 and 0.762 to 0.939, respectively. Molecular descriptors associated with electrostatic and topological properties are important to describe the antifungal activities of the phenolic compounds studied.

Quantitative structure-activity relationship (QSAR) and design of novel ligands that demonstrate high potency and target selectivity as protein tyrosine phosphatase 1B (PTP 1B) inhibitors as an effective strategy used to model anti-diabetic agents

Diabetes and obesity have increased dramatically in recent decades worldwide. Diabetes mainly emerged as a major health care burden disease in both the US and other industrialized countries, among which type II diabetes is the most common. Discovering new and effective treatments for diabetes is currently a high international health priority. In the present study a computational technique was used to model 97 compounds with PTP-1B inhibitory activity, in order to demonstrate the Quantitative structure-activity relationship (QSAR) of these compounds a genetic function approximation (GFA) algorithm was applied to pick the best descriptors and multiple linear regression (MLR) was used to establish a relationship between the PTP-1B inhibitory activity of these compounds and the best molecular descriptors. This QSAR study allowed investigating the influence of very simple and easy-to-compute descriptors in determining biological activities, which shed light on the key factors that aid in the design of novel potent molecules using computer-aided drug design tools.

Synthesis, Antitumor Evaluation, Molecular Modeling and Quantitative Structure-Activity Relationship (QSAR) of Novel 2-[(4-Amino-6-N-substituted-1,3,5-triazin-2-yl)methylthio]-4-chloro-5-methyl-N-(1H-benzo[d]imidazol-2(3H)-ylidene)Benzenesulfonamides.

A series of novel 2-[(4-amino-6-R2-1,3,5-triazin-2-yl)methylthio]-4-chloro-5-methyl-N-(5-R1-1H-benzo[d]imidazol-2(3H)-ylidene)benzenesulfonamides 6–49 was synthesized by the reaction of 5-substituted ethyl 2-{5-R1-2-[N-(5-chloro-1H-benzo[d]imidazol-2(3H)-ylidene)sulfamoyl]-4-methylphenylthio}acetate with appropriate biguanide hydrochlorides. The most active compounds, 22 and 46, showed significant cytotoxic activity and selectivity against colon (HCT-116), breast (MCF-7) and cervical cancer (HeLa) cell lines (IC50: 7–11 µM; 15–24 µM and 11–18 µM), respectively. Further QSAR (Quantitative Structure–Activity Relationships) studies on the cytotoxic activity of investigated compounds toward HCT-116, MCF-7 and HeLa were performed by using different topological (2D) and conformational (3D) molecular descriptors based on the stepwise multiple linear regression technique (MLR). The QSAR studies allowed us to make three statistically significant and predictive models for them. Moreover, the molecular docking studies were carried out to evaluate the possible binding mode of the most active compounds, 22 and 46, within the active site of the MDM2 protein.