scholarly journals QSAR study for anti-HIV-1 activities of HEPT derivatives using MLR and PLS

2013 ◽  
Vol 78 (4) ◽  
pp. 495-506 ◽  
Author(s):  
Daniela Ivan ◽  
Luminita Crisan ◽  
Simona Funar-Timofei ◽  
Mircea Mracec
Keyword(s):  
Goodness Of Fit ◽  
Correlation Coefficients ◽  
Predictive Ability ◽  
Qsar Model ◽  
Qsar Study ◽  
Molecular Features ◽  
Dragon Descriptors ◽  
Anti Hiv ◽  
Hept Derivatives ◽  
Hiv 1

A QSAR study using Multiple Linear Regression (MLR) and a Partial Least Squares (PLS) methodology was performed for a series of 127 derivatives of 1-(2-hydroxy-ethoxy)methyl]-6-(phenylthio)-timine (HEPT), a potent inhibitor of the of the human immunodeficiency virus type 1, HIV-1 reverse transcriptase (RT). To explore the relationship between a pool of HEPT derivative descriptors (as independent variables) and anti-HIV-1 activity expressed as log (1/EC50), as dependent variable) MLR and PLS methods have been employed. Using Dragon descriptors, the present study aims to develop a predictive and robust QSAR model for predicting anti-HIV activity of the HEPT derivatives for better understanding the molecular features of these compounds important for their biological activity. According to the squared correlation coefficients, which had values between 0.826 and 0.809 for the MLR and PLS methods, the results demonstrate almost identical qualities and good predictive ability for both MLR and PLS models. After dividing the dataset into training and test sets, the model predictability was tested by several parameters, including the Golbraikh-Tropsha external criteria and the goodness of fit tested with the Y-randomization test.

THE EXPLORATION OF CYP17A1 LIGAND SPACE BY THE QSAR MODEL

2021 ◽  
Author(s):  
Natalia Boboriko ◽  
◽  
He Liying ◽  
Yaraslau Dzichenka
Keyword(s):  
Hydrophobic Effect ◽  
Predictive Ability ◽  
Distance Matrix ◽  
Qsar Model ◽  
Aromatic Rings ◽  
Qsar Study ◽  
Test Set ◽  
Highly Active ◽  
High Efficient ◽  
F Measure

Cytochrome P450 17A1 (CYP17A1) is a critically important enzyme in humans that catalyzes the formation of all endogenous androgens. This enzyme is often considered a molecular target for the development of novel high efficient drugs against prostate cancer. In the present work, the random forest algorithm was used to conduct a QSAR study on 370 CYP17A1 ligands with different structures that were collected from the literature and databases, and a QSAR model was created based on the five important descriptors screened out – 2D adjacency and distance matrix descriptors, 2D atom counts and bond counts and 3D surface area, volume and shape descriptors. The model was verified by the test set (accuracy, specificity, sensitivity, F-measure, MCC, and AUC were calculated). It was revealed that the hydrophobic properties of the vdW surface of the ligand have a significant contribution to the activity prediction. The hydrophobic effect of the molecules may be aroused by the presence of the hydrophobic groups or aromatic rings in the molecules. The created QSAR model shows that the molecules with more aromatic rings have better activity. The accuracy of the model on the test set was 84%, precision – 81%, sensitivity – 93%, specificity – 72%, F-measure – 0.87, MCC – 0.67, AUC – 0.88. The model has good robustness and predictive ability and can be used to screen and discover new highly active CYP17A1 inhibitors.

In silico study on indole derivatives as anti HIV-1 agents: a combined docking, molecular dynamics and 3D-QSAR study

2013 ◽  
Vol 37 (8) ◽  
pp. 1001-1015 ◽  
Author(s):  
Anand Balupuri ◽  
Changdev G. Gadhe ◽  
Pavithra K. Balasubramanian ◽  
Gugan Kothandan ◽  
Seung Joo Cho
Keyword(s):  
Molecular Dynamics ◽  
In Silico ◽  
3D Qsar ◽  
Indole Derivatives ◽  
Qsar Study ◽  
In Silico Study ◽  
Anti Hiv ◽  
Hiv 1

Structure Activity Design, Synthesis and Biological Activity of Newer Imidazole-Triazine Clubbed Derivative as Antimicrobial and Antitubercular Agents

2021 ◽  
Vol 18 ◽  
Author(s):  
Jaydeep A. Patel ◽  
Navin B. Patel ◽  
Pratik K. Maisuriya ◽  
Monika R. Tiwari ◽  
Amit C. Purohit
Keyword(s):  
Thermodynamic Properties ◽  
Pharmacological Activity ◽  
Predictive Ability ◽  
Antitubercular Activity ◽  
Qsar Model ◽  
Total Surface Area ◽  
Lead Target ◽  
Antitubercular Agents ◽  
Qsar Study ◽  
2D Qsar

Background: Imidazole and triazine derivatives act as antimicrobial and antitubercular agents. 2D-QSAR determination estimates the pharmacological activity on the basis of thermodynamic properties of the structure. Objective: The structural arrangements and thermodynamic properties of the imidazole derivatives are necessary for the enhancement of pharmacological activity. So imidazole-triazine clubbed derivatives were designed on the bases of molecular modeling 2D-QSAR study of antitubercular activity. Methods: PLSR method was applied for 2D-QSAR determination of the (Z)-5-ethylidene-3-(4-methoxy-6-methyl-1,3,5-triazin-2-yl)-2-phenyl-3,5-dihydro-4H-imidazol-4-one (B1-B10). The designed compounds were synthesized and spectrally evicted by IR, 1H NMR, 13C NMR, Mass spectra data as well as biologically screened opposite different antitubercular and antimicrobial species. Result: Compounds B4, B6, B7 were founds potent against different antimicrobial species. Compound B3 was more effective against M. tuberculosis H37Rv. Statistically significant QSAR model generated by PLSR methods shows external r2=0.9775 and internal q2=0.2798 predictive ability. Whereas, the model incorporates with three parameters PolarSurfaceAreaExcluding P and S, MomInertiaY and SsCH3count with their corresponding values for each molecule. Conclusion: 2D-QSAR study advised antitubercular activity directly proportional to total surface area of the polar atoms having molecules and moment of inertia on Y-axis. Whereas, inversely proportional to methyl group joined with single bond. The present study afforded favorable results which were further used to generate lead target molecules.

A new, fully validated and interpreted quantitative structure-activity relationship model of p-aminosalicylic acid derivatives as neuraminidase inhibitors

2013 ◽  
Vol 67 (5) ◽  
Author(s):  
Ana Hartmman ◽  
Daniela Jornada ◽  
Eduardo Melo
Keyword(s):  
Predictive Ability ◽  
3D Qsar ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Neuraminidase Inhibitors ◽  
Aminosalicylic Acid ◽  
Randomization Tests ◽  
Qsar Study ◽  
Relationship Model ◽  
Mechanism Of Interaction

AbstractA multivariate QSAR study with a set of 34 p-aminosalicylic acid derivatives, described as neuraminidase inhibitors of the H1N1 viruses, is presented in this work. The variable selection was performed with the Ordered Predictors Selection (OPS) algorithm and the model was built with the Partial Least Squares (PLS) regression method. Leave-N-out cross-validation and y-randomization tests showed that the model was robust and free from chance correlation. The external predictive ability was superior to the 3D-QSAR model previously published. Moreover, it was possible to perform a mechanistic interpretation, where the descriptors referred directly to the mechanism of interaction with the neuraminidase.

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.

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.

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