Evaluation of contribution of different molecular fragments on antibacterial activity of Schiff bases of indole-3-aldehyde based on QSAR study

2013 ◽  
Vol 91 (12) ◽  
pp. 1174-1178
Author(s):  
Priyanka Kamaria ◽  
Neha Kawathekar
Keyword(s):  
Antibacterial Activity ◽  
Schiff Bases ◽  
Positive Contribution ◽  
Qsar Study ◽  
Qsar Analysis ◽  
Modeling Software ◽  
Qsar Models ◽  
Leave One Out ◽  
3D Package ◽  
Dipole Energy

The paper describes the QSAR analysis of a series of 22 Schiff bases of indole-3-aldehyde employing the Hansch approach. Various physicochemical and steric parameters were calculated using the Chem 3D package of molecular modeling Software Chemoffice 2004. QSAR models were generated employing the sequential multiple regression method. Models were validated using leave-one-out and bootstrapping methods. Results obtained show that dipole–dipole energy, LUMO, and total energy play an important role, as their positive contribution is seen in the models. Findings of the present study reveal that substituents that cause increase in flexibility, a decrease in polarity, and electron withdrawing in nature are favorable for antibacterial activity of Schiff bases.

STRUCTURAL OPTIMIZATION OF 6 SUBSTITUTED 2-AMINOBENZOTHIAZOLE DERIVATIVES AS ANTIFUNGAL AGENTS

INDIAN DRUGS ◽  
2016 ◽  
Vol 53 (10) ◽  
pp. 12-15
Author(s):  
B Jacob ◽  
◽  
V. Chandy ◽  
L. K. Bisht ◽  
H. Babu ◽  
...  
Keyword(s):  
Partition Coefficient ◽  
Quantitative Structure Activity Relationship ◽  
Homo Energy ◽  
Relationship Analysis ◽  
Multiple Regression Method ◽  
Modeling Software ◽  
Qsar Models ◽  
Statistical Program ◽  
Leave One Out ◽  
3D Package

In the present research fifteen analogues of 6 substituted 2-aminobenzothiazole derivatives displaying variable inhibition of Candida albicans were subjected to quantitative structure activity relationship analysis. Various thermodynamic, electronic and steric parameters were calculated using Chem 3D package of molecular modeling software Chemoffice 8.0. QSAR models were generated employing sequential multiple regression method using in-house statistical program VALSTAT. Statistically significant models with R-values(0.984), R2-(0.9699) and Q2 (0.848) were obtained. Models were validated using leave one out and bootstrapping methods. Results obtained shows that partition coefficient, HOMO energy and VDW Energy are contributing to biological activity. Findings of present study reveals that substituents those alters partition coefficient, HOMO energy and VDW Energy of molecule results in increase in antifungal potency.

DESIGN OF NEW PEROXISOME PROLIFERATORS GAMMA ACTIVATED RECEPTOR AGONISTS (PPARγ) VIA QSAR BASED MODELING

2018 ◽  
pp. 23-26 ◽  
Author(s):  
Tripathi RB ◽  
Jain J ◽  
Siddiqui AW
Keyword(s):  
Molecular Descriptor ◽  
Binding Pocket ◽  
Peroxisome Proliferators ◽  
Qsar Study ◽  
Qsar Analysis ◽  
Qsar Models ◽  
Artery Disease ◽  
Peroxisome Proliferators Activated Receptors ◽  
Recent Attention

The Peroxisome proliferators-activated receptors (PPARs) are one of the nuclear fatty acid receptors, which contain a type II zincfinger DNA binding pattern and a hydrophobic ligand binding pocket. These receptors are thought to play an essential role in metabolic diseasessuch as obesity, insulin resistance, and coronary artery disease. Therefore Peroxisome Proliferators-Activated Receptor (PPARγ) activators havedrawn great recent attention in the clinical management of type 2 diabetes mellitus, prompting several attempts to discover and optimize newPPARγ activators. Objective: The aim of the study was to finding new selective human PPARγ (PPARγ) modulators that are able to improveglucose homeostasis with reduced side effects compared with TZDs and identify the specific molecular descriptor and structural constraint toimprove the agonist activity of PPARγ analogs. Material and Method: Software’s that was used for this study include S.P. Gupta QSARsoftware (QSAR analysis), Valstat (Comparative QSAR analysis and calculation of L-O-O, Q2, r2, Spress), BILIN (Comparative QSAR analysisand calculation of Q2, r, S, Spress, and F), etc., allowing directly performing statistical analysis. Then multiple linear regression based QSARsoftware (received from BITS-Pilani, India) generates QSAR equations. Result and Discussion: In this study, we explored the quantitativestructure–activity relationship (QSAR) study of a series of meta-substituted Phenyl-propanoic acids as Peroxisome Proliferators Gamma activatedreceptor agonists (PPARγ).The activities of meta-substituted Phenyl-propanoic acids derivatives correlated with various physicochemical, electronic and steric parameters.Conclusion: The identified QSAR models highlighted the significance of molar refractivity and hydrophobicity to the biological activity.

scholarly journals A QSAR Study Based on SVM for the Compound of Hydroxyl Benzoic Esters

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Li Wen ◽  
Qing Li ◽  
Wei Li ◽  
Qiao Cai ◽  
Yong-Ming Cai
Keyword(s):  
Standard Deviation ◽  
Correlation Coefficient ◽  
Nonlinear Models ◽  
Predictive Ability ◽  
Quantitative Structure Activity Relationship ◽  
Support Vector ◽  
Qsar Study ◽  
Stability Robustness ◽  
Qsar Models ◽  
Leave One Out

Hydroxyl benzoic esters are preservative, being widely used in food, medicine, and cosmetics. To explore the relationship between the molecular structure and antibacterial activity of these compounds and predict the compounds with similar structures, Quantitative Structure-Activity Relationship (QSAR) models of 25 kinds of hydroxyl benzoic esters with the quantum chemical parameters and molecular connectivity indexes are built based on support vector machine (SVM) by using R language. The External Standard Deviation Error of Prediction (SDEPext), fitting correlation coefficient (R2), and leave-one-out cross-validation (Q2LOO) are used to value the reliability, stability, and predictive ability of models. The results show that R2 and Q2LOO of 4 kinds of nonlinear models are more than 0.6 and SDEPext is 0.213, 0.222, 0.189, and 0.218, respectively. Compared with the multiple linear regression (MLR) model (R2=0.421, RSD = 0.260), the correlation coefficient and the standard deviation are both better than MLR. The reliability, stability, robustness, and external predictive ability of models are good, particularly of the model of linear kernel function and eps-regression type. This model can predict the antimicrobial activity of the compounds with similar structure in the applicability domain.

scholarly journals 2D-QSAR Study of Indolylpyrimidines Derivative as Antibacterial against Pseudomonas aeruginosa and Staphylococcus aureus: A Comparative Approach

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Prasanna A. Datar
Keyword(s):  
Staphylococcus Aureus ◽  
Pseudomonas Aeruginosa ◽  
Qsar Model ◽  
Antibacterial Activities ◽  
Quantitative Structure Activity Relationship ◽  
Comparative Approach ◽  
Qsar Study ◽  
Qsar Analysis ◽  
Leave One Out ◽  
And Staphylococcus Aureus

A set of 15 indolylpyrimidine derivatives with their antibacterial activities in terms of minimum inhibitory concentration against the gram-negative bacteria Pseudomonas aeruginosa and gram-positive Staphylococcus aureus were selected for 2D quantitative structure activity relationship (QSAR) analysis. QSAR was performed using a combination of various descriptors such as steric, electronic and topological. Stepwise regression method was used to derive the most significant QSAR equation for predicting the inhibitory activity of this class of molecules. The best QSAR model was further validated by a leave one out technique as well as by the random trials. A high correlation between experimental and predicted inhibitory values was observed. A comparative picture of behavior of indolylpyrimidines against both of the microorganisms is discussed.

scholarly journals QSAR Analysis of 5-Substituted-2-Benzoyl-aminobenzoic acids as PPAR Modulator

2004 ◽  
Vol 1 (5) ◽  
pp. 243-250 ◽  
Author(s):  
R. Hemalatha ◽  
L. K. Soni ◽  
A. K. Gupta ◽  
S. G. Kaskhedikar
Keyword(s):  
Linear Regression Analysis ◽  
Qsar Model ◽  
Multiple Linear Regression Analysis ◽  
Quantitative Structure Activity Relationship ◽  
Statistical Regression ◽  
Qsar Study ◽  
Qsar Analysis ◽  
Aminobenzoic Acids ◽  
Leave One Out ◽  
Α Activity

A quantitative structure activity relationship (QSAR) study on a series of analogs of 5-aryl thiazolidine-2, 4-diones with activity on PPAR-α and PPAR-γwas made using combination of various thermodynamic, electronic and spatial descriptors. Several statistical regression expressions were obtained using multiple linear regression analysis. The best QSAR model was further validated by leave one out cross validation method. The studied revealed that for dual PPAR-α/γactivity dipole-dipole energy and PMI-Z play significant role and contributed positively for PPAR-γand PPAR-α activity respectively. Thus, QSAR brings important structural insight to aid the design of dual PPAR-α /γreceptor agonist.

scholarly journals QSAR modeling of antibacterial activity of some benzimidazole derivatives

2011 ◽  
Vol 17 (1) ◽  
pp. 33-38 ◽  
Author(s):  
Sanja Podunavac-Kuzmanovic ◽  
Dragoljub Cvetkovic
Keyword(s):  
Antibacterial Activity ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Structural Features ◽  
Benzimidazole Derivatives ◽  
Qsar Modeling ◽  
Qsar Study ◽  
Validation Procedure ◽  
Leave One Out

A quantitative structure-activity relationship (QSAR) study has been carried out for training set of 12 benzimidazole derivatives to correlate and predict the antibacterial activity of studied compounds against Gram-negative bacteria Pseudomonas aeruginosa. Multiple linear regression was used to select the descriptors and to generate the best prediction model that relates the structural features to inhibitory activity. The predictivity of the model was estimated by cross-validation with the leave-one-out method. Our results suggest a QSAR model based on the following descriptors: parameter of lipophilicity (logP) and hydration energy (HE). Good agreement between experimental and predicted inhibitory values, obtained in the validation procedure, indicated the good quality of the generated QSAR model.

Structural finding of R/S-3,4-dihydro-2,2-dimethyl-6-halo-4-(substituted phenylaminocarbonylamino)-2H-1-benzopyrans as selective pancreatic β-cells KATP-pβ channel openers

2007 ◽  
Vol 85 (12) ◽  
pp. 1053-1063 ◽  
Author(s):  
Sk. Mahasin Alam ◽  
Soma Samanta ◽  
Amit Kumar Halder ◽  
Soumya Basu ◽  
Tarun Jha
Keyword(s):  
Insulin Secretion ◽  
Negative Impact ◽  
Inhibitory Effect ◽  
Van Der Waals Interactions ◽  
Β Cells ◽  
Pancreatic Β Cells ◽  
Qsar Study ◽  
Water Partition Coefficient ◽  
Qsar Models ◽  
Leave One Out

R/S-3,4-Dihydro-2,2-dimethyl-6-halo-4-(substituted phenylaminocarbonyl-amino)-2H-1-benzopyrans are pancreatic β-cells potassium (KATP-pβ) channel openers with inhibitory effect on insulin secretion. To find the more active and effective benzopyrans as selective potassium (KATP-pβ) channel openers towards the pancreatic tissues, quantitative structure–activity relationships (QSAR) study was performed using E-state and R-state indices along with Wang–Ford charges, n-octanol/water partition coefficient, molar refractivity, and indicator parameters. QSAR models were developed by statistical techniques, e.g., multiple linear regression (MLR), principle component regression analysis (PCRA), and partial least squares (PLS) analysis. The generated equations were validated by the leave-one-out cross-validation method. The models show the importance of ETSA indices of atom numbers 16, 17, 18, 19, 21 as well as 22. The positive coefficient of S16, S17, S18, S19, S21, and S22 indicate that with the increase of the value of E-state indices, desired activity decreases. RTSA index is also important for the biological activity, and the atom numbers 16, 17, 18, 19, 20 and 22 are involved in van der Waals interactions. RTSA index also possesses negative impact on the inhibition of residual insulin secretion. Wang–Ford charges of some particular atoms are also important for the inhibition. Increase of n-octanol/water partition coefficients of compounds inhibit insulin secretion, and the presence of chlorine atom at m- and p- positions of the phenyl ring B is necessary for the inhibition of residual insulin secretion.Key words: benzopyran derivatives, potassium channel openers, PCRA, PLS, QSAR.

scholarly journals Linear and Nonlinear QSAR Study of N2 and O6 Substituted Guanine Derivatives as Cyclin-Dependent Kinase 2 Inhibitors

2013 ◽  
Vol 2013 ◽  
pp. 1-8
Author(s):  
Nasser Goudarzi ◽  
M. Arab Chamjangali ◽  
Payam Kalhor
Keyword(s):  
Randomization Test ◽  
Cyclin Dependent Kinase ◽  
Ann Model ◽  
Statistical Parameters ◽  
Qsar Study ◽  
Guanine Derivatives ◽  
Qsar Models ◽  
Test Sets ◽  
The Mean ◽  
Leave One Out

The inhibitory activities (pIC50) of N2 and O6 substituted guanine derivatives as cyclin-dependent kinase 2 (CDK2) inhibitors have been successfully modeled using calculated molecular descriptors. Two linear (MLR) and nonlinear (ANN) methods were utilized for construction of models to predict the pIC50 activities of those compounds. The QSAR models were validated by cross-validation (leave-one-out) as well as application of the models for prediction of pIC50 of external set compounds. Also, the models were validated by calculation of statistical parameters and Y-randomization test. Two methods provided accurate predictions, although more accurate results were obtained by ANN model. The mean-squared errors (MSEs) for validation and test sets of MLR are 0.065, 0.069 and of ANN are 0.017 and 0.063, respectively.

scholarly journals QSAR Study of (5-Nitroheteroaryl-1,3,4-Thiadiazole-2-yl) Piperazinyl Derivatives to Predict New Similar Compounds as Antileishmanial Agents

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Abdellah Ousaa ◽  
Bouhya Elidrissi ◽  
Mounir Ghamali ◽  
Samir Chtita ◽  
Adnane Aouidate ◽  
...  
Keyword(s):  
External Validation ◽  
Correlation Coefficients ◽  
Predictive Ability ◽  
Quantitative Structure Activity Relationship ◽  
Qsar Study ◽  
Qsar Analysis ◽  
Ann Models ◽  
Artificial Neural Network Ann ◽  
Leave One Out ◽  
New Compounds

To search for newer and potent antileishmanial drugs, a series of 36 compounds of 5-(5-nitroheteroaryl-2-yl)-1,3,4-thiadiazole derivatives were subjected to a quantitative structure-activity relationship (QSAR) analysis for studying, interpreting, and predicting activities and designing new compounds using several statistical tools. The multiple linear regression (MLR), nonlinear regression (RNLM), and artificial neural network (ANN) models were developed using 30 molecules having pIC50 ranging from 3.155 to 5.046. The best generated MLR, RNLM, and ANN models show conventional correlation coefficients R of 0.750, 0.782, and 0.967 as well as their leave-one-out cross-validation correlation coefficients RCV of 0.722, 0.744, and 0.720, respectively. The predictive ability of those models was evaluated by the external validation using a test set of 6 molecules with predicted correlation coefficients Rtest of 0.840, 0.850, and 0.802, respectively. The applicability domains of MLR and MNLR transparent models were investigated using William’s plot to detect outliers and outsides compounds. We expect that this study would be of great help in lead optimization for early drug discovery of new similar compounds.

scholarly journals Application of QSAR models in analysis of antibacterial activity of some benzimidazole derivatives against Sarcina lutea

2013 ◽  
Vol 67 (1) ◽  
pp. 27-33
Author(s):  
Sanja Podunavac-Kuzmanovic ◽  
Dragoljub Cvetkovic ◽  
Lidija Jevric ◽  
Natasa Uzelac
Keyword(s):  
Antibacterial Activity ◽  
Inhibitory Activity ◽  
Antibacterial Activities ◽  
Quantitative Structure Activity Relationship ◽  
Benzimidazole Derivatives ◽  
Log P ◽  
Statistical Parameters ◽  
Qsar Analysis ◽  
Qsar Models

In the present paper, a quantitative structure activity relationship (QSAR) has been carried out on a series of 2-methyl and 2-aminobenzimidazole derivatives to identify the lipophilicity requirements for their inhibitory activity against bacteria Sarcina lutea. The tested compounds displayed in vitro antibacterial activity and minimum inhibitory concentration (MIC) was determined for all compounds. The partition coefficients of the studied compounds were measured by the shake flask method (log P) and by theoretical calculation (Clog P). The relationships between lipophilicity descriptors and antibacterial activities were investigated and the mathematical models have been developed as a calibration models for predicting the inhibitory activity of this class of compounds. The models were validated by leave-one-out (LOO) technique as well as by the calculation of statistical parameters for the established models. Therefore, QSAR analysis reveals that lipophilicity descriptor govern the inhibitory activity of benzimidazoles studied against Sarcina lutea.

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