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 QSAR studies for the acute toxicity of nitrobenzenes to the Tetrahymena pyriformis

2014 ◽  
Vol 79 (9) ◽  
pp. 1111-1125 ◽  
Author(s):  
Dan-Dan Wang ◽  
Lin-Lin Feng ◽  
Guang-Yu He ◽  
Hai-Qun Chen
Keyword(s):  
Statistical Significance ◽  
External Validation ◽  
Tetrahymena Pyriformis ◽  
Quantitative Structure Activity Relationship ◽  
Partial Least Square ◽  
Molecular Structures ◽  
Least Square ◽  
Qsar Studies ◽  
Qsar Models ◽  
Leave One Out

Quantitative structure-activity relationship (QSAR) models play a key role in finding the relationship between molecular structures and the toxicity of nitrobenzenes to Tetrahymena pyriformis. In this work, genetic algorithm, along with partial least square (GA-PLS) was employed to select optimal subset of descriptors that have significant contribution to the toxicity of nitrobenzenes to Tetrahymena pyriformis. A set of five descriptors, namely G2, HOMT, G(Cl?Cl), Mor03v and MAXDP, was used for the prediction of the toxicity of 45 nitrobenzene derivatives and then were used to build the model by multiple linear regression (MLR) method. It turned out that the built model, whose stability was confirmed using the leave-one-out validation and external validation test, showed high statistical significance (R2=0.963, Q2LOO=0.944). Moreover, Y-scrambling test indicated there was no chance correlation in this 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.

scholarly journals QSAR studies combined with DFT-calculations and Molecular docking of polyamine-sensitive inhibitors of the NMDA receptor

2019 ◽  
Vol 9 (3) ◽  
pp. 164-174
Author(s):  
Mohamed Mazigh ◽  
Charif El’mbarki ◽  
Hanine Hadni ◽  
Menana Elhallaoui
Keyword(s):  
Molecular Docking ◽  
Nmda Receptor ◽  
Molecular Descriptors ◽  
Correlation Coefficients ◽  
Dft Method ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Quantitative Model ◽  
Qsar Studies ◽  
Leave One Out

A quantitative structure-activity relationship (QSAR) was carried out to analyze inhibitory activity of 35 compounds, new polyamine-sensitive inhibitors of the NMDA receptor, using multiple linear regression (MLR), artificial neural networks (NN), and the molecular descriptors were calculated using DFT method. This study shows that the compounds' activity correlates reasonably well with six selected descriptors by MLR method. The correlation coefficients calculated by MLR and after that by NN, R =0.878 and R =0.978 respectively, are relatively kind to evaluate the proposed quantitative model, and to predict activity for new polyamine-sensitive inhibitors of the NMDA receptor. The test of the performance of the NN model, using a cross-validation method with a leave-one-out procedure (LOO) shows that the predictive power of this model is relevant (R=0.966). The constitutional molecular descriptors (nN and nHBD) have the most significant impact in the formulation of the QSAR model. The molecular docking investigations exploring the influence of the structural differences in the interaction potency demonstrate that the number of N atoms expressed by multiple hydrogen bonds helps the ligand to be fixed to NR2B subtype of NMDA receptor.

An Introduction to the Basic Concepts in QSAR-Aided Drug Design

2015 ◽  
pp. 1-47 ◽  
Author(s):  
Maryam Hamzeh-Mivehroud ◽  
Babak Sokouti ◽  
Siavoush Dastmalchi
Keyword(s):  
Drug Design ◽  
Drug Development ◽  
Biological Activities ◽  
Model Development ◽  
Quantitative Structure Activity Relationship ◽  
Molecular Structures ◽  
New Drugs ◽  
Qsar Modeling ◽  
Qsar Studies ◽  
Modern Drug

The need for the development of new drugs to combat existing and newly identified conditions is unavoidable. One of the important tools used in the advanced drug development pipeline is computer-aided drug design. Traditionally, to find a drug many ligands were synthesized and evaluated for their effectiveness using suitable bioassays and if all other drug-likeness features were met, the candidate(s) would possibly reach the market. Although this approach is still in use in advanced format, computational methods are an indispensable component of modern drug development projects. One of the methods used from very early days of rationalizing the drug design approaches is Quantitative Structure-Activity Relationship (QSAR). This chapter overviews QSAR modeling steps by introducing molecular descriptors, mathematical model development for relating biological activities to molecular structures, and model validation. At the end, several successful cases where QSAR studies were used extensively are presented.

An Introduction to the Basic Concepts in QSAR-Aided Drug Design

Oncology ◽  
2017 ◽  
pp. 20-66
Author(s):  
Maryam Hamzeh-Mivehroud ◽  
Babak Sokouti ◽  
Siavoush Dastmalchi
Keyword(s):  
Drug Design ◽  
Drug Development ◽  
Biological Activities ◽  
Model Development ◽  
Quantitative Structure Activity Relationship ◽  
Molecular Structures ◽  
New Drugs ◽  
Qsar Modeling ◽  
Qsar Studies ◽  
Modern Drug

The need for the development of new drugs to combat existing and newly identified conditions is unavoidable. One of the important tools used in the advanced drug development pipeline is computer-aided drug design. Traditionally, to find a drug many ligands were synthesized and evaluated for their effectiveness using suitable bioassays and if all other drug-likeness features were met, the candidate(s) would possibly reach the market. Although this approach is still in use in advanced format, computational methods are an indispensable component of modern drug development projects. One of the methods used from very early days of rationalizing the drug design approaches is Quantitative Structure-Activity Relationship (QSAR). This chapter overviews QSAR modeling steps by introducing molecular descriptors, mathematical model development for relating biological activities to molecular structures, and model validation. At the end, several successful cases where QSAR studies were used extensively are presented.

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.

QSAR Models to Predict Effect of Concentration on the Adsorption of Phenolic Compounds onto XAD-4 and ZH-01

2011 ◽  
Vol 356-360 ◽  
pp. 340-344
Author(s):  
Yun Lan Gu ◽  
Zhen Xing Li ◽  
Zheng Hao Fei ◽  
Gen Cheng Zhang
Keyword(s):  
Phenolic Compounds ◽  
Adsorption Capacity ◽  
External Validation ◽  
Quantitative Structure Activity Relationship ◽  
Basis Set ◽  
Stepwise Multiple Regression ◽  
Background Concentration ◽  
Qsar Models ◽  
Predicted Values ◽  
Leave One Out

It is assumed that the experimental adsorption capacity of phenolic compounds onto resin depends upon the molecular properties as well as background concentration of the aquatic system. The utility of this concept has been demonstrated by incorporating concentration as a parameter in quantitative structure-activity relationship (QSAR). DFT-B3LYP method, with the basis set 6-311G **, was employed to calculate quantum mechanical and physicochemical descriptors of phenolic compounds. The logarithm of the adsorption capacity of phenolic compounds on XAD-4 and ZH-01 investigated from the static experiment along with the descriptors mentioned above were used to establish QSAR models. The variables were reduced using stepwise multiple regression method, and the statistical results indicated that the correlation coefficient in the multiple linear regression (MLR) and cross-validation using leave-one-out(LOO) were 0.966, 0.920, 0.905 and 0.797, respectively. To validate the predictive power of resulting models, external validation was performed with Qext2 values of 0.927 and 0.849, respectively. The developed models suggest that the adsorption mechanism of phenolic compounds onto XAD-4 and ZH-01 is different. Concentration, hydrophobic parameter are dominant factors governing the adsorption capacity of phenolic compounds onto XAD-4, while concentration and energy of the highest occupied molecular orbital are dominant factors controlling that of phenolic compounds on ZH-01. The consistency between experimental and predicted values indicates that the developed models can be used for estimating adsorption capacity of phenolic compounds onto XAD-4 and ZH-01.

Predicting Impact Sensitivity of Heterocyclic Nitroarenes from Molecular Structures Selected by Genetic Algorithm

2012 ◽  
Vol 535-537 ◽  
pp. 2550-2553
Author(s):  
Rui Wang ◽  
Yong Gu Wang ◽  
Hui Liu
Keyword(s):  
Genetic Algorithm ◽  
Cross Validation ◽  
Correlation Coefficients ◽  
Molecular Structures ◽  
Impact Sensitivity ◽  
Optimal Subset ◽  
Leave One Out ◽  
The Impact ◽  
Prediction Capability ◽  
Good Agreement

A novel theoretical model was constructed to predict the impact sensitivity of 44 heterocyclic nitroarenes. The optimal subset of the molecular structures descriptors were selected by genetic algorithm (GA). The multiple linear regression (MLR) was then applied to build a prediction model of impact sensitivity for the 44 compounds. The correlation coefficients (R2) together with correlation coefficient of the leave-one-out cross validation (Q2CV) of the model is 0.928 and 0.865, respectively. The new model is highly statistically significant, and the robustness as well as internal prediction capability of which is satisfactory. The predicted impact sensitivity values are in good agreement with the experimental data.

QSAR Studies on Thiazole Derivatives as HCV NS5A Inhibitors via CoMFA and CoMSIA Methods

2019 ◽  
Vol 16 (4) ◽  
pp. 453-460 ◽  
Author(s):  
Jiayu Li ◽  
Wenyue Tian ◽  
Diaohui Gao ◽  
Yuying Li ◽  
Yiqun Chang ◽  
...  
Keyword(s):  
Nonstructural Protein ◽  
External Validation ◽  
Three Dimensional ◽  
3D Qsar ◽  
Quantitative Structure Activity Relationship ◽  
Field Analysis ◽  
Thiazole Derivatives ◽  
Hydrogen Bond Acceptor ◽  
Qsar Studies ◽  
Comfa Model

Background: Hepatitis C Virus (HCV) infection is the major cause of hepatitis after transfusion. And HCV Nonstructural Protein 5A (NS5A) inhibitors have become a new hotspot in the study of HCV inhibitors due to their strong antiviral activity, rapid speed of viral removing and broad antiviral spectrum. Methods: Forty-five NS5A inhibitors were chosen to process three-dimensional quantitative structure- activity relationship (3D-QSAR) by using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models. A training set consisting of 30 compounds was applied to establish the models and a test set consisting of 15 compounds was applied to do the external validation. Results: The CoMFA model predicted a q2 value of 0.607 and an r2 value of 0.934. And the CoMSIA model predicted a q2 value of 0.516 and an r2 value of 0.960 established on the effects of steric, electrostatic, hydrophobic and hydrogen-bond acceptor. 0.713 and 0.939 were the predictive correlation co-efficients (r2pred) of CoMFA and CoMSIA models, respectively. Conclusion: These conclusions provide a theoretical basis for drug design and screening of HCV NS5A complex inhibitors.

QSPR Study on Gross Heat of Combustion of Nitro Aromatic Compounds Based on Genetic Algorithm

2013 ◽  
Vol 750-752 ◽  
pp. 2248-2251
Author(s):  
Rui Wang ◽  
Hong Yin Cao ◽  
Quan Sheng Kang ◽  
Zhen Ming Li
Keyword(s):  
Genetic Algorithm ◽  
Aromatic Compounds ◽  
Correlation Coefficients ◽  
Molecular Structures ◽  
Heat Of Combustion ◽  
Qspr Model ◽  
Optimal Subset ◽  
Leave One Out ◽  
Prediction Capability ◽  
Nitro Aromatic

A novel QSPR model was proposed as to predict the gross heat of combustion of 32 nitro aromatic compounds. Genetic algorithm (GA) was applied to select the optimal subset of the molecular structures descriptors most related to gross heat of combustion. The multiple linear regression (MLR) was taken to build a prediction model of gross heat of combustion for the 32 compounds. The correlation coefficients (R2) together with correlation coefficient of the leave-one-out cross validation (Q2CV) of the model is 0.997 and 0.995, respectively. The new model is highly statistically significant, and the robustness as well as internal prediction capability of which is satisfactory. This study can provide a new way for predicting the gross heat of combustion of nitro aromatic compounds for engineering.

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