Mehrab Noorizadeh

Young Researchers Club, Teheran, Tehrān, Iran

Are you Mehrab Noorizadeh?

Claim your profile

Publications (11)23.43 Total impact

  • [Show abstract] [Hide abstract]
    ABSTRACT: Genetic algorithm and partial least square (GA-PLS) and Levenberg- Marquardt artificial neural network (L-M ANN) techniques were used to investigate the correlation between retention time and descriptors for drug metabolites which obtained by two-dimensional liquid chromatography. The applied internal (leave-group-out cross validation (LGO-CV)) and external (test set) validation methods were used for the predictive power of four models. Both methods resulted in accurate prediction whereas more accurate results were obtained by L-M ANN model. The best model obtained from L-M ANN showed a good R(2) value (determination coefficient between observed and predicted values) for all compounds, which was superior to GA-PLS models. Copyright © 2011 John Wiley & Sons, Ltd.
    Drug Testing and Analysis 10/2011; · 3.17 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: A quantitative structure-retention relationship (QSRR) study based on an artificial neural network (ANN) was carried out for the prediction of the ultra-performance liquid chromatography-Time-of-Flight mass spectrometry (UPLC-TOF-MS) retention time (RT) of a set of 52 pharmaceuticals and drugs of abuse in hair. The genetic algorithm was used as a variable selection tool. A partial least squares (PLS) method was used to select the best descriptors which were used as input neurons in neural network model. For choosing the best predictive model from among comparable models, square correlation coefficient R(2) for the whole set calculated based on leave-group-out predicted values of the training set and model-derived predicted values for the test set compounds is suggested to be a good criterion. Finally, to improve the results, structure-retention relationships were followed by a non-linear approach using artificial neural networks and consequently better results were obtained. This also demonstrates the advantages of ANN. Copyright © 2011 John Wiley & Sons, Ltd.
    Drug Testing and Analysis 09/2011; · 3.17 Impact Factor
  • H Noorizadeh, A Farmany, M Noorizadeh, M Kohzadi
    [Show abstract] [Hide abstract]
    ABSTRACT: A quantitative structure-property relationship (QSPR) study based on an artificial neural network (ANN) was carried out for the prediction of the microemulsion liquid chromatography polar surface area (PSA) of a set of 32 drug compounds. The genetic algorithm-kernel partial least squares (GA-KPLS) method was used as a variable selection tool. A KPLS method was used to select the best descriptors and the selected descriptors were used as input neurons in neural network model. For choosing the best predictive model from among comparable models, square correlation coefficient Q(2) for the whole set calculated based on leave-group-out predicted values of the training set and model-derived predicted values for the test set compounds is suggested to be a good criterion. Finally, to improve the results, structure-property relationships were followed by nonlinear approach using artificial neural networks and consequently better results were obtained. Also this demonstrates the advantages of ANN. Copyright © 2011 John Wiley & Sons, Ltd.
    Drug Testing and Analysis 05/2011; · 3.17 Impact Factor
  • H Noorizadeh, A Farmany, M Noorizadeh
    [Show abstract] [Hide abstract]
    ABSTRACT: Genetic algorithm and partial least square (GA-PLS), kernel PLS (GA-KPLS) and Levenberg- Marquardt artificial neural network (L-M ANN) techniques were used to investigate the correlation between dissociation constant (pK(a) ) and descriptors for 60 drug compounds. The applied internal (leave-group-out cross validation (LGO-CV)) and external (test set) validation methods were used for the predictive power of models. Descriptors of GA-KPLS model were selected as inputs in L-M ANN model. The results indicate that L-M ANN can be used as an alternative modeling tool for quantitative structure-property relationship (QSPR) studies. Copyright © 2011 John Wiley & Sons, Ltd.
    Drug Testing and Analysis 04/2011; · 3.17 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Genetic algorithm (GA) and partial least squares (PLS) and kernel PLS (KPLS) techniques were used to investigate the correlation between immobilized liposome chromatography partitioning (log Ks) and descriptors for 65 drug compounds. The models were validated using leave-group-out cross validation LGO-CV. The results indicate that GA-KPLS can be used as an alternative modelling tool for quantitative structure-property relationship (QSPR) studies. Copyright © 2011 John Wiley & Sons, Ltd.
    Drug Testing and Analysis 03/2011; · 3.17 Impact Factor
  • Source
    Hadi Noorizadeh, Mehrab Noorizadeh, Adnan S. Mumtaz
    [Show abstract] [Hide abstract]
    ABSTRACT: Genetic algorithm and partial least square (GA–PLS) and Levenberg–Marquardt artificial neural network (L–M ANN) techniques were used to investigate the correlation between capacity factor (k′) and descriptors for 40 nanoparticle compounds which obtained by comprehensive two-dimensional gas chromatography (GC × GC) stationary phases consisting of thin films of gold-centered monolayer protected nanoparticles (MPNs) system. The applied internal (leave-group-out cross-validation (LGO-CV)) and external (test set) validation methods were used for the predictive power of models. The results indicate that L–M ANN can be used as an alternative modeling tool for quantitative structure–retention relationship (QSRR) studies. This is the first research on the QSRR of the nanoparticle compounds using the L–M ANN.
    Journal of Saudi Chemical Society 01/2011; · 1.29 Impact Factor
  • Source
    Hadi Noorizadeh, Abbas Farmany, Mehrab Noorizadeh
    [Show abstract] [Hide abstract]
    ABSTRACT: Recebido em 7/3/10; aceito em 12/8/10; publicado na web em 30/11/10 Genetic algorithm and multiple linear regression (GA-MLR), partial least square (GA-PLS), kernel PLS (GA-KPLS) and Levenberg-Marquardt artificial neural network (L-M ANN) techniques were used to investigate the correlation between retention index (RI) and descriptors for 116 diverse compounds in essential oils of six Stachys species. The correlation coefficient LGO-CV (Q 2) between experimental and predicted RI for test set by GA-MLR, GA-PLS, GA-KPLS and L-M ANN was 0.886, 0.912, 0.937 and 0.964, respectively. This is the first research on the QSRR of the essential oil compounds against the RI using the GA-KPLS and L-M ANN.
    Química Nova 01/2011; 34:242-249. · 0.74 Impact Factor
  • Source
    Hadi Noorizadeh, Abbas Farmany, Mehrab Noorizadeh
    [Show abstract] [Hide abstract]
    ABSTRACT: Genetic algorithm and partial least square (GA-PLS) and kernel PLS (GA-KPLS) techniques were used to investigate the correlation between retention indices (RI) and descriptors for 117 diverse compounds in essential oils from 5 Pimpinella species gathered from central Turkey which were obtained by gas chromatography and gas chromatography-mass spectrometry. The square correlation coefficient leave-group-out cross validation (LGO-CV) (Q2) between experimental and predicted RI for training set by GA-PLS and GA-KPLS was 0.940 and 0.963, respectively. This indicates that GA-KPLS can be used as an alternative modeling tool for quantitative structure-retention relationship (QSRR) studies.
    Química Nova 12/2010; 34(8):1398-1404. · 0.74 Impact Factor
  • Hadi Noorizadeh, Mehrab Noorizadeh
    [Show abstract] [Hide abstract]
    ABSTRACT: The quantitative structure-retention relationship (QSRR) of 69 opiate and sedative drugs against the comprehensive two-dimensional gas chromatography retention time (RT) was studied. The genetic algorithm (GA) was employed to select the variables that resulted in the best-fitted models. After the variables were selected, the linear multivariate regressions [e.g., the multiple linear regression (MLR), the partial least squares (PLS)] as well as the nonlinear regressions [e.g., the kernel PLS (KPLS), Levenberg–Marquardt artificial neural network (L–M ANN)] were utilized to construct the linear and nonlinear QSRR models. The correlation coefficient LGO-CV (Q2) of prediction for the GA-KPLS and L–M ANN models for training and test sets were (0.921 and 0.960) and (0.892 and 0.925), respectively, revealing the reliability of these models. The obtained results using L-M ANN were compared with those of GA-MLR, GA-PLS, and GA-KPLS, exhibiting that the L–M ANN model demonstrated a better performance than that of the other models. The resulting data indicated that L–M ANN could be used as a powerful modeling tool for the QSRR studies. This is the first research on the QSRR of the drug compounds against the RT using the GA-KPLS and L–M ANN. KeywordsOpiate and sedative drugs–Comprehensive two-dimensional gas chromatography–QSRR–Genetic algorithm-kernel partial least squares–Levenberg–Marquardt artificial neural network
    Medicinal Chemistry Research · 1.61 Impact Factor
  • Hadi Noorizadeh, Abbas Farmany, Mehrab Noorizadeh
    [Show abstract] [Hide abstract]
    ABSTRACT: The hazardous psychoactive designer drugs are compounds in which part of the molecular structure of a stimulant or narcotic has been modified. Genetic algorithm and kernel partial least square (GA–KPLS) and Levenberg–Marquardt artificial neural network (L–M ANN) techniques were used to investigate the correlation between capacity factor (k′) and descriptors for 104 hazardous psychoactive designer drugs. These drugs are containing Tryptamine, Phenylethylamine, and Piperazine. The both methods resulted in accurate prediction whereas more accurate results were obtained by L–M ANN model. The best model obtained from L–M ANN showed a good R 2 value (determination coefficient between observed and predicted values) for all compounds, which was superior to GA–KPLS models. The stability and prediction ability of these models were validated using leave-group-out cross-validation, external test set, and Y-randomization techniques. This is the first research on the quantitative structure–retention relationship (QSRR) of the designer drugs using the GA–KPLS and L–M ANN.
    Medicinal Chemistry Research 21(9). · 1.61 Impact Factor
  • Hadi Noorizadeh, Mehrab Noorizadeh, Abbas Farmany
    [Show abstract] [Hide abstract]
    ABSTRACT: A toxicology screen checks a person’s blood or urine or both for the presence of drugs or other toxic substances. The screen can determine the type and amount of drugs or other toxic substances a person may have swallowed, injected, or inhaled. A quantitative structure–retention relationship (QSRR) was developed using the partial least square (PLS), Kernel PLS (KPLS), and Levenberg–Marquardt artificial neural network (L–M ANN) approach for the study of chemometrics. The data which contained retention time (RT) of the 175 toxicological screening of basic drugs in whole blood and tested on authentic samples were obtained by ultra performance liquid chromatography coupled with time-of-flight mass spectrometry. Genetic algorithm (GA) was employed as a factor selection procedure for PLS and KPLS modeling methods. By comparing the results, GA-PLS descriptors are selected for L–M ANN. Finally, a model with a low prediction error and a good correlation coefficient was obtained by L–M ANN. The stability and prediction ability of L–M ANN model were validated using external test set and Y-randomization techniques. The described model does not require experimental parameters and potentially provides useful prediction for RT of new compounds. This is the first research on the QSRR of toxicological screening of basic drugs in whole blood using the chemometrics models.
    Medicinal Chemistry Research 21(12). · 1.61 Impact Factor

Publication Stats

9 Citations
23.43 Total Impact Points

Institutions

  • 2011
    • Young Researchers Club
      Teheran, Tehrān, Iran
    • Islamic Azad University
      • Chemistry
      Teheran, Tehrān, Iran
  • 2010
    • Islamic Azad University of Ilam
      Elām, Īlām, Iran