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.

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.

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.

A QSAR STUDY OF SUBSTITUTED PYRAZOLINE DERIVATIVES AS POTENTIAL ANTI-TUBERCULOSIS AGENTS

INDIAN DRUGS ◽  
2017 ◽  
Vol 54 (04) ◽  
pp. 22-31
Author(s):  
M. C Sharma ◽  
Keyword(s):  
Statistical Significance ◽  
External Validation ◽  
Model Development ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Partial Least Square ◽  
Least Square ◽  
Qsar Study ◽  
Partial Least Square Analysis ◽  
Qsar Models

A quantitative structure–activity relationship (QSAR) of a series of substituted pyrazoline derivatives, in regard to their anti-tuberculosis activity, has been studied using the partial least square (PLS) analysis method. QSAR model development of 64 pyrazoline derivatives was carried out to predict anti-tubercular activity. Partial least square analysis was applied to derive QSAR models, which were further evaluated for statistical significance and predictive power by internal and external validation. The best QSAR 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 0.7426 and 0.7903, respectively. Two-dimensional QSAR analyses of such pyrazoline derivatives provide important structural insights for designing potent antituberculosis drugs.

scholarly journals QSAR Study of Anthra[1,9-cd]pyrazol-6(2H)-one Derivatives as Potential Anticancer Agents Using Statistical Methods

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
El Ghalia Hadaji ◽  
Abdelkarim Ouammou ◽  
Mohammed Bouachrine
Keyword(s):  
Anticancer Agents ◽  
Density Functional ◽  
Cross Validation ◽  
Prediction Models ◽  
Structural Parameters ◽  
Three Dimensional ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Topological Descriptors ◽  
Qsar Study

In this study, the anticancer activity of a series of 32 molecules based on anthra[1,9-cd]pyrazol-6(2H)-one was studied by three-dimensional quantitative structure-activity relationship (QSAR) analyses: multiple linear regression (MLR), partial least squares (PLS), multiple nonlinear regression (MNLR), cross-validation analyses, and Y-randomization. A theoretical study of series was firstly studied using density functional theory (DFT) calculations at B3LYP/6-31 level of theory for employing to determine the structural parameters and electronic properties. Then the topological descriptors were computed using ACD/ChemSketch and ChemDraw 8.0 programs. The RNLM, given the descriptors obtained from the MLR and PLS, exhibited a correlation coefficient close to 0.91. The prediction models collected were confirmed by two methods of cross-validation and scrambling (or Y-randomization). The strong correlation between experimental and predicted activity values was observed, indicating the validation and good quality of the derived QSAR model.

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.

Theoretical Bioevaluation of 1,2,4-Thiadiazole-1,2,4-triazole Derivatives via Molecular Modelling Approach

2021 ◽  
Vol 6 (1) ◽  
pp. 40-46
Author(s):  
Oyebamiji Abel Kolawole ◽  
Akintelu Sunday Adewale ◽  
Simon N. Odoemene ◽  
Oyeneyin Oluwatoba Emmanuel ◽  
Semire Banjo
Keyword(s):  
Breast Cancer ◽  
Docking Study ◽  
Qsar Model ◽  
Qsar Study ◽  
Triazole Derivatives ◽  
Discovery Studio ◽  
Artificial Neural Network Ann ◽  
Type 3 ◽  
Better Than ◽  
Modelling Approach

Breast cancer still remains one of the precarious ailments among humans globally. The vulnerability of this ailment in homeopathic world remains colossal and this has drawn the attention of seasoned researchers to find lasting solution to this hazard. Therefore, 10 novel 1,2,4-thiadiazole-1,2,4-triazole derivatives were studied so as to explore their anti-breast cancer activities. The studied compounds were optimized using Spartan 14 and the QSAR study was executed by using Gretl and MATLAB. Also, docking study was observed using Pymol (for treating downloaded protein), Autodock Tool (for locating binding site in the downloaded protein and for converting ligand and receptor to .pdbqt format from .pdb format), Auto dock vina (for docking calculation) and discovery studio (for viewing the nonbonding interaction between the docked complexes). The selected descriptors were used to developed effective QSAR model and it was observed that the developed QSAR model using artificial neural network (ANN) predicted better than the prediction made by multiple linear regression (MLR). More so, the calculated binding affinity revealed that compound g (-11.4 kcal/mol) possess ability to inhibit 3α-hydroxysteroid dehydrogenase type 3 (PDB ID: 4xo6) than other studied compounds as well as etoposide (Standard).

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 An In Silico Model for Predicting Drug-Induced Hepatotoxicity

2019 ◽  
Vol 20 (8) ◽  
pp. 1897 ◽  
Author(s):  
Shuaibing He ◽  
Tianyuan Ye ◽  
Ruiying Wang ◽  
Chenyang Zhang ◽  
Xuelian Zhang ◽  
...  
Keyword(s):  
Large Scale ◽  
Characteristic Curve ◽  
External Validation ◽  
Model Development ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
New Drugs ◽  
Drug Induced ◽  
Drug Candidates ◽  
External Test

As one of the leading causes of drug failure in clinical trials, drug-induced liver injury (DILI) seriously impeded the development of new drugs. Assessing the DILI risk of drug candidates in advance has been considered as an effective strategy to decrease the rate of attrition in drug discovery. Recently, there have been continuous attempts in the prediction of DILI. However, it indeed remains a huge challenge to predict DILI successfully. There is an urgent need to develop a quantitative structure–activity relationship (QSAR) model for predicting DILI with satisfactory performance. In this work, we reported a high-quality QSAR model for predicting the DILI risk of xenobiotics by incorporating the use of eight effective classifiers and molecular descriptors provided by Marvin. In model development, a large-scale and diverse dataset consisting of 1254 compounds for DILI was built through a comprehensive literature retrieval. The optimal model was attained by an ensemble method, averaging the probabilities from eight classifiers, with accuracy (ACC) of 0.783, sensitivity (SE) of 0.818, specificity (SP) of 0.748, and area under the receiver operating characteristic curve (AUC) of 0.859. For further validation, three external test sets and a large negative dataset were utilized. Consequently, both the internal and external validation indicated that our model outperformed prior studies significantly. Data provided by the current study will also be a valuable source for modeling/data mining in the future.

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.

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