List of Articles ارتباط کمی ساختار- خاصیت

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  1 - Camputational methods and quantitative structure–property relationship study for prediction of melting point of carbocyclic nitroaromatic compounds using chemical and quantum mechanics descriptors: combining DFT and QSPR calculations
  mehdi nekoei mehdi maham
  The DFT-B3LYP method, with the base set 6-31G (d), was used to calculate several quantum chemical descriptors of 60 compounds of carbocyclic nitroaromatics. A suitable set of quantum mechanics and chemical descriptors was calculated and quantitative structure–prop More

  The DFT-B3LYP method, with the base set 6-31G (d), was used to calculate several quantum chemical descriptors of 60 compounds of carbocyclic nitroaromatics. A suitable set of quantum mechanics and chemical descriptors was calculated and quantitative structure–property relationship method for prediction of melting point of carbocyclic nitroaromatic compounds by multiple linear regression (MLR) and support vector machine (SVM) were studied. At first, structure of the compounds were plotted and of quantum mechanics descriptors was calculated and the stepwise method was employed to select those descriptors that resulted in the best fitted models. At first, we developed linear model of MLR. Then the selected molecular descriptors were used as inputs for SVM. The obtained results using SVM were compared with MLR which revealed superiority of the SVM model over the MLR method. Manuscript profile
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  2 - Quantitative Structure-Property Relationship Study for Prediction of the Solvent Polarity Using Quantum Mechanics Descriptors and Support Vector Machine
  mehdi nekoei بهزاد چهکندی
  Quantitative structure-property relationship (QSPR) study for prediction of the polarity some of solvents using quantum mechanics descriptors and support vector machine. Experimental S′ values for 69 solvents were assembled. This set included saturated and unsatur More

  Quantitative structure-property relationship (QSPR) study for prediction of the polarity some of solvents using quantum mechanics descriptors and support vector machine. Experimental S′ values for 69 solvents were assembled. This set included saturated and unsaturated hydrocarbons, solvents containing halogen, cyano, nitro, amide, sulfide, mercapto, sulfone, phosphate, ester, ether, etc. After drawing the structure of the molecules, the suitable molecular descriptors were calculated. Then, the stepwise multiple linear regressions (SW-MLR) variable selection method was subsequently employed to select and implement the prominent descriptors having the most significant contributions to the polarity of the molecules. At first, multiple linear regressions (MLR) model was constructed. Then, support vector machine (SVM) model was used for to obtain better results. A comparison of results by the two methodologies indicated the superiority of SW-SVM over the SW-MLR method. Manuscript profile
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  3 - Modeling and quantitative structure-property relationship (QSPR) study to predict the acidic constants of some chemical compounds using multiple linear regression and support vector machine
  mehdi nekoei Abbass Taheri Majid Mohammadhosseini
  Modeling and studying the structure-property quantitative relationship (QSPR) to predict the acidic constants of some chemical compounds were performed using multiple linear regression (MLR) and support vector machine (SVM). First, the structure of chemical compounds wa More

  Modeling and studying the structure-property quantitative relationship (QSPR) to predict the acidic constants of some chemical compounds were performed using multiple linear regression (MLR) and support vector machine (SVM). First, the structure of chemical compounds was plotted and a suitable group of descriptors was calculated. Then, the step selection method was used to obtain the best descriptors that were most related to the chemical properties of the compounds. Then, linear multiple linear regression (MLR) model and nonlinear vector machine (SVM) model were used to predict the acid constants of the compounds. Statistical data showed that the SVM method was superior to the MLR method. Manuscript profile
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  4 - Modeling and quantitative structure-property relationship studying to predict the half-life of polychlorinated biphenyls using multivariate linear regression and artificial neural networks
  سکینه بهرامی نسب مهدی نکوئی سیدعباس طاهری
  Quantitative structure-property relationship (QSPR) study was performed to predict the half-life of some polychlorinated biphenyl derivatives using multivariate linear regression (MLR) and artificial neural networks (ANN). First, the structure of the compounds, the draw More

  Quantitative structure-property relationship (QSPR) study was performed to predict the half-life of some polychlorinated biphenyl derivatives using multivariate linear regression (MLR) and artificial neural networks (ANN). First, the structure of the compounds, the drawing, and the appropriate group of descriptors were calculated. Then, the step-wise method was used to obtain the best descriptors that were most related to the half-life of the compounds. With this method, 6 descriptors including Lop, GATS5m, GATS8m, LDip, RDF020u, R2v + were selected from the types of topological descriptors, charge, three-dimensional representation of molecules based on electron diffraction and radial distribution function. First, a multiple linear regression linear model was constructed. Then, artificial neural network was used to obtain better results. The values of coefficient of determination (R2) and root mean square error (RMSE) for the test series were equal to 0.716 and 0.050 for the MLR linear model and 0.896 and 0.030 for the nonlinear ANN model, respectively. Statistical data show the superiority of ANN method over MLR method. Manuscript profile