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 Organic Compounds Based on (E)-N-Aryl-2-ethene-sulfonamide as Microtubule Targeted Agents in Prostate Cancer: QSAR Study

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
El Ghalia Hadaji ◽  
Mohamed Bourass ◽  
Abdelkarim Ouammou ◽  
Mohammed Bouachrine
Keyword(s):  
Anticancer Agents ◽  
Molecular Descriptor ◽  
External Validation ◽  
Correlation Coefficients ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Qsar Study ◽  
Anticancer Properties ◽  
Base Matrix ◽  
Artificial Neural Network Ann

(E)-N-Aryl-2-ethene-sulfonamide and its derivatives are potent anticancer agents; these compounds inhibit cancer cells proliferation. A study of quantitative structure-activity relationship (QSAR) has been applied on 40 compounds based on (E)-N-Aryl-2-ethene-sulfonamide, in order to predict their anticancer biological activity. The principal components analysis is used for minimizing the base matrix and the multiple linear regression (MLR) and multiple nonlinear regression have been used to design the relationships between the molecular descriptor and anticancer properties of the sulfonamide derivatives. The validation of the models MLR and MNLR has been done by dividing the dataset into training and test set, the external validation of multiple correlation coefficients was RpIC50 = 0.81 for MLR and RpIC50 = 0.91 for MNLR. The artificial neural network (ANN) showed a correlation coefficient close to 0.96, which concluded that this latter model is more effective and much better than the other models. This obtained model (ANN) has been confirmed by two methods of LOO cross-validation and scrambling (or Y-randomization). The high correlation between experimental and predicted activity values was observed, indicating the validation and the good quality of the derived QSAR model.

Study of Peptides QSAR Based on Multidimensional Attributes (E) Using Multiple Linear Regression

2011 ◽  
Vol 345 ◽  
pp. 263-269 ◽  
Author(s):  
Jia Jian Yin
Keyword(s):  
Amino Acids ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Correlation Coefficients ◽  
Meaningful Work ◽  
Quantitative Structure Activity Relationship ◽  
Structure Characterization ◽  
Qsar Study ◽  
Qsar Analysis ◽  
Leave One Out

A new amino acids descriptor E, which (E1~E5) has been introduced in bioactive peptides Quantitative Structure-Activity Relationship (QSAR) Study. It has been proved that correlate good with hydrophobicity, size, preference for amino acids to occur in -helices, composition and the net charge, respectively. They were then applied to construct characterization and QSAR analysis on 48 bitter tasting dipeptides and 30 bradykinin potentiating (BP) pentapeptides using multiple linear regression (MLR). The leave-one-out cross validation values (Q2(CV)) were 0.888 and 0.797, the multiple correlation coefficients (R2) were 0.940 and 0.891, respectively for bitter tasting dipeptides and BP pentapeptides. The results showed that, in comparison with the conventional descriptors, the descriptor (E) is a useful structure characterization method for peptide QSAR analysis. The importance of each property at each position in peptides is estimated by the regression coefficient value of the MLR model. The establishment of such methods will be a very meaningful work to peptide bioactive investigation in peptide drug design.

QSAR Studies of Halogenated Pyrimidine Derivatives as Inhibitors of Human Dihydroorotate Dehydrogenase Using Modified Bee Algorithm

2018 ◽  
Vol 21 (5) ◽  
pp. 381-387 ◽  
Author(s):  
Hossein Atabati ◽  
Kobra Zarei ◽  
Hamid Reza Zare-Mehrjardi
Keyword(s):  
External Validation ◽  
Correlation Coefficients ◽  
Quantitative Structure Activity Relationship ◽  
Molecular Structures ◽  
Dihydroorotate Dehydrogenase ◽  
Pyrimidine Derivatives ◽  
Qsar Studies ◽  
Bee Algorithm ◽  
Test Sets ◽  
Leave One Out

Aim and Objective: Human dihydroorotate dehydrogenase (DHODH) catalyzes the fourth stage of the biosynthesis of pyrimidines in cells. Hence it is important to identify suitable inhibitors of DHODH to prevent virus replication. In this study, a quantitative structure-activity relationship was performed to predict the activity of one group of newly synthesized halogenated pyrimidine derivatives as inhibitors of DHODH. Materials and Methods: Molecular structures of halogenated pyrimidine derivatives were drawn in the HyperChem and then molecular descriptors were calculated by DRAGON software. Finally, the most effective descriptors for 32 halogenated pyrimidine derivatives were selected using bee algorithm. Results: The selected descriptors using bee algorithm were applied for modeling. The mean relative error and correlation coefficient were obtained as 2.86% and 0.9627, respectively, while these amounts for the leave one out−cross validation method were calculated as 4.18% and 0.9297, respectively. The external validation was also conducted using two training and test sets. The correlation coefficients for the training and test sets were obtained as 0.9596 and 0.9185, respectively. Conclusion: The results of modeling of present work showed that bee algorithm has good performance for variable selection in QSAR studies and its results were better than the constructed model with the selected descriptors using the genetic algorithm method.

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 Mechanistic and Predictive QSAR Analysis of Diverse Molecules to Capture Salient and Hidden Pharmacophores for Anti-Thrombotic Activity

2021 ◽  
Vol 22 (15) ◽  
pp. 8352
Author(s):  
Magdi E. A. Zaki ◽  
Sami A. Al-Hussain ◽  
Vijay H. Masand ◽  
Manoj K. Sabnani ◽  
Abdul Samad
Keyword(s):  
External Validation ◽  
Factor Xa ◽  
Predictive Ability ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Data Set ◽  
Qsar Analysis ◽  
Lead Compounds ◽  
Charged Ring ◽  
Life Threatening

Thrombosis is a life-threatening disease with a high mortality rate in many countries. Even though anti-thrombotic drugs are available, their serious side effects compel the search for safer drugs. In search of a safer anti-thrombotic drug, Quantitative Structure-Activity Relationship (QSAR) could be useful to identify crucial pharmacophoric features. The present work is based on a larger data set comprising 1121 diverse compounds to develop a QSAR model having a balance of acceptable predictive ability (Predictive QSAR) and mechanistic interpretation (Mechanistic QSAR). The developed six parametric model fulfils the recommended values for internal and external validation along with Y-randomization parameters such as R2tr = 0.831, Q2LMO = 0.828, R2ex = 0.783. The present analysis reveals that anti-thrombotic activity is found to be correlated with concealed structural traits such as positively charged ring carbon atoms, specific combination of aromatic Nitrogen and sp2-hybridized carbon atoms, etc. Thus, the model captured reported as well as novel pharmacophoric features. The results of QSAR analysis are further vindicated by reported crystal structures of compounds with factor Xa. The analysis led to the identification of useful novel pharmacophoric features, which could be used for future optimization of lead compounds.

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.

Study of Pyrimidine-4-carboxamide Derivatives as HIV-1 Integrase Inhibitors Using QSAR and DFT Calculations

2018 ◽  
Vol 3 (1) ◽  
pp. 119-133 ◽  
Author(s):  
B. Elidrissi ◽  
A. Ousaa ◽  
M. Ghamali ◽  
S. Chtita ◽  
M. A. Ajana ◽  
...  
Keyword(s):  
Density Functional ◽  
External Validation ◽  
Quantitative Structure Activity Relationship ◽  
Ann Model ◽  
Qsar Study ◽  
Test Set ◽  
Physico Chemical ◽  
Pca Method ◽  
Artificial Neural Network Ann ◽  
Hiv 1

A Quantitative Structure–Activity Relationship (QSAR) study was performed to predict HIV-1 integrase inhibition activity (pIC50) of thirty-five 5-hydroxy-6-oxo-1,6-dihydropyrimidine-4-carboxamide compounds using the electronic and physico-chemical descriptors computed respectively, with Gaussian 03W and ACD/ChemSketch programs. The structures of all compounds were optimized using the hybrid Density Functional Theory (DFT) at the B3LYP/6-31G(d) level of theory. In both approaches, 28 compounds were assigned as the training set and the rest as the test set. These compounds were analyzed by the principal components analysis (PCA) method, the descendant Multiple Linear Regression (MLR) analyses and the Artificial Neural Network (ANN). The robustness of the obtained models was assessed by leave-many-out cross-validation, and external validation through a test set. This study shows that the MLR has served marginally better to predict pIC50 activity, when compared with the results given by predictions made with a (4-3-1) ANN model.

QSPR/QSAR Study of Mercaptans by Quantum Topological Method

2011 ◽  
Vol 233-235 ◽  
pp. 2536-2540
Author(s):  
Xuan Chen ◽  
Chang Ming Nie ◽  
Song Nian Wen
Keyword(s):  
Density Functional ◽  
Cross Validation ◽  
Correlation Coefficients ◽  
Predictive Ability ◽  
Molecular Structures ◽  
Structure Property ◽  
Qsar Study ◽  
Validation Parameters ◽  
The Stability ◽  
Leave One Out

A new molecular quantum topological index QT was constructed by molecular topological methods and quantum mechanics (QM), which together with Gibbs free energy(G), Constant volume mole hot melting(CV) that were calculated by density functional theory (DFT) at the B3LYP/6-31G(d) level of theory for mercaptans. Index QT can not only efficiently distinguish molecular structures of mercaptans, but also possess good applications of QSPR/QSAR (quantitative structure-property/activity relationships). And most of the correlation coefficients of the models were over 0.99. The LOO CV (leave-one-out cross-validation) method was used to testify the stability and predictive ability of the models. The validation results verified the good stability and predictive ability of the models employing the cross-validation parameters: RCV, SCVand FCV, which demonstrated the wide potential of the index QT for applications to QSPR/ QSAR.

scholarly journals QSAR and docking studies of 3, 5-dimethylpyrazole as potent inhibitors of Phosphodiesterase-4

2021 ◽  
Vol 11 (1-s) ◽  
pp. 86-93
Author(s):  
Hiba Hashim Mahgoub Mohamed ◽  
Amna Bint Wahab Elrashid Mohammed Hussien ◽  
Ahmed Elsadig Mohammed Saeed
Keyword(s):  
Molecular Docking ◽  
Correlation Coefficient ◽  
External Validation ◽  
Quantitative Structure Activity Relationship ◽  
Docking Studies ◽  
Pyrazole Derivatives ◽  
Qsar Study ◽  
Furan Moiety ◽  
The Stability ◽  
Leave One Out

A quantitative structure-activity relationship (QSAR) study was performed to develop a model on a series of 3, 5-dimethylpyrazole containing furan moiety derivatives which exhibited considerable inhibitory activity against PDE4B. The obtained model has correlation coefficient (r) of 0.934, squared correlation coefficient (r2) of 0.872, and leave-one-out (LOO) cross-validation coefficient (Q2) value of 0.733. The predictive power of the developed model was confirmed by the external validation which has (r2) value of 0.812. These parameters confirm the stability and robustness of the model to predict the activity of a new designed set of 3,5-dimethyl-pyrazole derivatives (I-XV), results indicated that the compound III, V, XIII, and XV showed the strongest inhibition activity (IC50 = 0.2813, 0.5814, 0.6929, 0.6125μM, respectively) against PDE4B compared to the reference rolipram with (IC50=1.9μM). Molecular docking was performed on a new designed compound with PDE4B protein (3o0j). Docking results showed that compounds (X and IX) have high docking affinity of -36.2037 and -33.2888 kcal/mol respectively. Keywords: QSAR, molecular docking, pyrazole derivatives, PDE4 inhibitors, anti-inflammatory.

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