Prediction of micelle–water partition coefficient from the theoretical derived molecular descriptors

Fatemi, M.; Karimian, F.

doi:10.1016/j.jcis.2007.06.047

Cited by 23 publications

(13 citation statements)

References 36 publications

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“…Before training the network, the parameters of the number of nodes in the hidden layer, weights and biases learning rates, and momentum were optimized. The procedure of optimization of these parameters was explained in our previous works [44,45]. Table 4 shows the architecture and specification of the optimized network.…”

Section: Non-linear Modelingmentioning

confidence: 99%

Prediction of supercritical fluid chromatographic retention factors at different percents of organic modifiers in mobile phase

Fatemi

Malekzadeh

Shamseddin

2009

J of Separation Science

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In this work quantitative structure-retention relationship models were developed to predict the solute retention factors in supercritical fluid chromatography (SFC) in various organic modifiers. Data set contains the retention data of 35 various organic compounds in 0, 2, 4 and 6% of methanol in mobile phase. The obtained 140 data points were divided into training, internal and external test sets which have 93, 23 and 24 retention data. The diversity validation test was performed on data the set to ensure that the structure of the training and/or test sets can represent those of the whole ones. Descriptors which were selected by stepwise multiple linear regression (MLR) methods are: the percent of organic modifier in mobile phase, salvation connectivity index chi-2, salvation connectivity index chi-5, H attached to heteroatom, the 2(nd) structural information content and polarity parameter. These descriptors were used as features in generation of linear and non-linear models using MLR and artificial neural network (ANN) methods, respectively. The root mean square error of MLR model are 0.116, 0.138 and 0.260 for training, internal and external test sets, respectively, while these values are 0.036, 0.097 and 0.244 for ANN model, respectively. Comparison between these values and other statistical parameters obtained from these two models reveals the credibility of ANN in prediction of solute retention factors in SFC.

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Section: Non-linear Modelingmentioning

confidence: 99%

Prediction of supercritical fluid chromatographic retention factors at different percents of organic modifiers in mobile phase

Fatemi

Malekzadeh

Shamseddin

2009

J of Separation Science

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“…An analysis of the correlation weights of their models indicates that vitamins are most likely to interact with water and octanol by more complex mechanisms than various compounds under consideration, which are not vitamins. Fatemi and Karimian [12] studied the micelle-water partition coefficients of 81 organic compounds in an SDS solution by the quantitative structure-property relationship method. Chen et al [13] correlated 209 molecular structure patterns of polychlorinated diphenyl ethers (PCDEs) with their n-octanol/water partition coefficient (lgK ow ) and sub-cooled liquid water solubilities (-logS w,l ) using the stepwise multiple regression (SMR).…”

Section: Introductionmentioning

confidence: 99%

Prediction of gas to water partition coefficient of some organic compounds using theoretically derived molecular descriptors

Dashtbozorgi

Golmohammadi

2012

J Struct Chem

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An artificial neural network (ANN) is constructed and trained for the prediction of gas to water partition coefficients of various organic compounds. The inputs of this neural network are theoretically derived from molecular descriptors that were chosen by the genetic algorithm-partial least squares (GA-PLS) feature selection technique. These descriptors are: area-weighted surface charge of hydrogen bonding donor atoms (HDCA-2), average bond order of a C atom (P C ), Kier flexibility index ()), atomic charge weighted partial positively charged surface area (PPSA-3), and difference between atomic charge weighted partial positive and negative surface areas (DPSA-3). By comparing the results obtained from PLS and ANN models, one can see that statistical parameters (Fisher ratio, correlation coefficient, and standard error) of the ANN model are better than those of the PLS model, which indicates that a nonlinear model can simulate more accurately the relationship between the structural descriptors and the partition coefficients of the investigated molecules.

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“…Performance of the QSPR model heavily depends on the computational method adopted to build the model [3]. Many different methods, such as multiple linear regression (MLR) [2,4], partial least square analysis (PLS) [5,6], multilayer perceptrons (MLP) neural network [6,7], radial basis function neural network (RBF NN) [8] Adaptive neuro-fuzzy inference system (ANFIS) [9][10][11], and support vector machine (SVM) [3,12], have been used in QSPR models.…”

Section: Introductionmentioning

confidence: 99%

Diffusion coefficient prediction of acids in water at infinite dilution by QSPR method

Khajeh

Rasaei

2011

Struct Chem

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In this article, quantitative structure-property relationship (QSPR) models were developed for estimation of diffusion coefficients (DCs) of acids at infinite dilution in water. These models were obtained based on molecular descriptors of a set of 65 compounds at 298.15 K. Five date splits are randomly extracted from this dataset. Using a genetic algorithm (GA) variable-selection approach, five molecular descriptors in each split were selected from a set of thousand of them. To analyze the nonlinear behavior of these molecular descriptors, Adaptive neuro-fuzzy inference system (ANFIS) and radial basis function neural network (RBF NN) were used. These models were trained on 52 arbitrary sets of the data, and the remaining 13 sets were employed to evaluate the proposed models. Genetic function approximation (GFA) method could predict DCs of test dataset with high value of squared correlation coefficients of 0.984. QSPR model even gave superior predictions. ANFIS and RBF NN methods when implemented in QSPR model provided the squared correlation coefficients of 0.989 and 0.987, respectively. It is therefore suitable to be used in the proposed QSPR models, especially when applied with AN-FIS method, to obtain DC of acids at infinite dilution in water instead of other expensive and complicated experiments.

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Prediction of micelle–water partition coefficient from the theoretical derived molecular descriptors

Cited by 23 publications

References 36 publications

Prediction of supercritical fluid chromatographic retention factors at different percents of organic modifiers in mobile phase

Prediction of supercritical fluid chromatographic retention factors at different percents of organic modifiers in mobile phase

Prediction of gas to water partition coefficient of some organic compounds using theoretically derived molecular descriptors

Diffusion coefficient prediction of acids in water at infinite dilution by QSPR method

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