فهرس المقالات GA-MLR

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  1 - QSAR study of camptothecin derivatives as anticancer drugs using genetic algorithm and multiple linear regression analysis
  fatemeh shafiei Shahaboddin Mohebbi Tahereh Momeni Isfahani Mehdi Ahmadi Sabegh
  10.30495/jptc.2023.71661.1252
  A quantitative structure- activity relationship (QSAR) has been widely used to investigation a correlation between chemical structures of molecules to their activities. In the present study, QSAR models have been carried out on 76 camptothecin (CPT) derivatives as antic أکثر

  A quantitative structure- activity relationship (QSAR) has been widely used to investigation a correlation between chemical structures of molecules to their activities. In the present study, QSAR models have been carried out on 76 camptothecin (CPT) derivatives as anticancer drugs to determine the 14N nucleus quadrupole coupling constants (QCC). These quantum chemical properties have been calculated using Density Functional Theory (DFT) and B3LYP/6-311G (d, p) method in the gas phase. A training set of 60 CPT derivatives were used to construct QSAR models and a test set of 16 compounds were used to evaluate the build models that were made using multiple linear regression (MLR) analysis. Molecular descriptors were calculated by Dragon software, and the stepwise multiple linear regression and the Genetic algorithm (GA) techniques were used to select the best descriptors and build QSAR models respectively. QSAR models were used to delineate the important descriptors responsible for the properties of the CPT derivatives. The statistically significant QSAR models derived by GA-MLR analysis were validated by Leave-One-Out Cross-Validation (LOOCV) and external validation methods. The multicollinearity of the descriptors contributed in the models was tested by calculating the variance inflation factor (VIF) and the DurbinWatson (DW) statistics. The predictive ability of the models was found to be satisfactory. The results of QSAR study show that quantum parameters, 2D autocorrelations and Walk and path counts descriptors contains important structural information sufficient to develop useful predictive models for the studied activities. تفاصيل المقالة
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  2 - Quantitative Structure- Property Relationship(QSPR) Study of 2-Phenylindole derivatives as Anticancer Drugs Using Molecular Descriptors
  samira Bahrami fatemeh shafiei Azam Marjani Tahereh Momeni Isfahani
  10.30495/jptc.2021.20876
  A QSPR study on a series of 2-Phenylindole derivatives as anticancer agents was performed to explore the important molecular descriptor which is responsible for their thermodynamic properties such as heat capacity (Cv) and entropy(S).Molecular descriptors were calculate أکثر

  A QSPR study on a series of 2-Phenylindole derivatives as anticancer agents was performed to explore the important molecular descriptor which is responsible for their thermodynamic properties such as heat capacity (Cv) and entropy(S).Molecular descriptors were calculated using DRAGON software and the Genetic Algorithm (GA) and backward selection procedure were used to reduce and select the suitable descriptors. Multiple Linear Regression (MLR) analysis was carried out to derive QSPR models, which were further evaluated for statistical significance such as squared correlation coefficient (R2) root mean square error (RMSE), adjusted correlation coefficient (R2adj) and fisher index of quality (F).The multicollinearity of the descriptors selected in the models were tested by calculating the variance inflation factor (VIF), Pearson correlation coefficient (PCC) and the DurbinWatson (DW) statistics. The predictive powers of the MLR models were discussed using Leave-One-Out Cross-Validation (LOOCV) and test set validation methods. The best QSPR models for prediction the Cv(J/molK) and S(J/molK), having squared correlation coefficient R2 =0.907 and 0.901, root mean squared error RMSE=2.019 and RMSE= 2.505, and cross-validated squared correlation coefficient R2 cv = 0.902 and 0.889, respectively. The statistical outcomes derived from the present study demonstrate good predictability and may be useful in the design of new 2-Phenylindole derivatives. تفاصيل المقالة
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  3 - A Priori Prediction of Tissue: Plasma Partition Coefficients (Log BP) of Drugs to Facilitate the Use of MLR and MLR-GA Methods
  Z. Bayat J. Movaffagh
  It is important to determine whether a candidate molecule is capable of penetrating the plasma-brain barrier indrug discovery and development. The aim of this paper is to establish a predictive model for plasma-brainbarrier penetration using simple descriptors The usefu أکثر

  It is important to determine whether a candidate molecule is capable of penetrating the plasma-brain barrier indrug discovery and development. The aim of this paper is to establish a predictive model for plasma-brainbarrier penetration using simple descriptors The usefulness of the quantum chemical descriptors, calculated atthe level of the DFT and HE theories using 6-310* basis set for QSAR study of anti-vial NucleosideAnalogues drugs was examined. Delivery of anti-viral agents into the central nervous system (CNS) is clmicallyhoportant. Nucleoside analogues are a major source of clinically used antiviral agents. The QSAR modeldeveloped contributed to a mechanistic understanding of the investigated biological effects. The first step in thisstudy was to use a dauset containing 23 drugs with known activity. In the next steps some of them with thelarge secondary chain branches were removed to make our approach. Multiple Linear Regressions (MLR) wasemployed to model the relationships between molecular descriptors and biological activities of molecules usingstepwise method and genetic algorithm as variable selection tools. Biological activities contain the logarithm ofthe ratio of the steady-state concentration of a compound in the brain to in the plasma, log Bp. A multiparametricequation containing maximum six descriptors at HF/6-3 10* and eight descriptors at 133LYP/6-3 10*method with good statistical qualities(Rpax— 0.976 , 11214n— 0.959 at HF/6-3/G* and Rwax4 0.979 , 112KGP 0.952 at B3LYP/6-316*) wasbtiledbyMl(plLR gr g teP thoi Th Sal d d ibis p pe ppears to bevery simple but robust and effective for predictive use This method relates log Bp values to fundamentalmolecular properties, such as Electrostatic Potential. Local charge, Electric Field Gradient, Isotropicparameters, Natural Population Analysis. Also, GA-MLR regression was used to model the structure — activityrelationships. تفاصيل المقالة