The Effect of Variable Selection on the Non‐linear Modelling of Oestrogen Receptor Binding

Ghafourian, Taravat; Cronin, Mark T.D.

doi:10.1002/qsar.200510153

Cited by 11 publications

(5 citation statements)

References 43 publications

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“…FIRM analysis used in this study has the advantage that it can take the nonlinear effects of structural properties into account (Hawkins et al, 1997;Blower et al, 2002) and has proven useful in classifying pharmaceutical data by discrete or continuous descriptors (Ghafourian and Cronin, 2006;Godden et al, 2003). For example, suppose hydrogen bonding groups aid aqueous solubility of a certain group of drugs to a certain extent.…”

Section: Discussionmentioning

confidence: 99%

Estimation of drug solubility in water, PEG 400 and their binary mixtures using the molecular structures of solutes

Ghafourian

Bozorgi

2010

European Journal of Pharmaceutical Sciences

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With the aim of solubility estimation in water, polyethylene glycol 400 (PEG) and their binary mixtures, Quantitative Structure -Property Relationships (QSPRs) were investigated to relate the solubility of a large number of compounds to the descriptors of the molecular structures. The relationships were quantified using linear regression analysis (with descriptors selected by stepwise regression) and Formal Inference -based Recursive Modeling (FIRM). The models were compared in terms of the solubility prediction accuracy for the validation set. The resulting regression and FIRM models employed a diverse set of molecular descriptors explaining crystal lattice energy, molecular size, and solute -solvent interactions. Significance of molecular shape in compound's solubility was evident from several shape descriptors being selected by FIRM and stepwise regression analysis. Some of these influential structural features, e.g. connectivity indexes and Balaban topological index, were found to be related to the crystal lattice energy. The results showed that regression models outperformed most FIRM models and produced higher prediction accuracy. However, the most accurate estimation was achieved by the use of a combination of FIRM and regression models. The results also showed that the use of melting point in regression models improves the estimation accuracy especially for solubility in higher concentrations of PEG. Aqueous or PEG/water solubilities can be estimated by these models with root mean square error of below 0.70.

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Section: Discussionmentioning

confidence: 99%

Estimation of drug solubility in water, PEG 400 and their binary mixtures using the molecular structures of solutes

Ghafourian

Bozorgi

2010

European Journal of Pharmaceutical Sciences

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“…Effective descriptor or variable selection is an integral part of the QSAR modeling process [8,9,12,18].…”

Section: Introductionmentioning

confidence: 99%

On Some Aspects of Variable Selection for Partial Least Squares Regression Models

2008

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This paper tries to explore the optimum variable selection strategy for Partial Least Squares (PLS) regression using a model dataset of cytoprotection data. The compounds of the dataset were classified using K-means clustering technique applied on standardized descriptor matrix and ten combinations of training and test sets were generated based on the obtained clusters. For a particular training set, PLS models were developed with a number of components optimized by leave-one-out Q 2 and then the developed models were validated (externally) using the test set compounds. For each set, PLS model was initially constructed using all descriptors (variables). The variables having least standardized values of regression coefficients were deleted and the next model was developed with a reduced set of variables. These steps were performed several times until further reduction in number of variables did not improve Q 2 value. In each case, statistical parameters like predictive R 2 (R 2 pred ), squared correlation coefficient between observed and predicted values with (r 2 ) and without (r 2 0 ) intercept and Root Mean Square Error of Prediction (RMSEP) were calculated from the test set compounds. In case of all ten sets, Q 2 values steadily increase on deletion of variables while R 2 pred values do not show any specific trend. In no case, the highest Q 2 and highest R 2 pred appear in the same trial, i.e., with the same combinations of variables. This suggests that from the viewpoint of external predictability, choice of variables for PLS based on Q 2 value may not be optimum. Moreover, a clear separation of r 2 and r 0 2 curves in some sets suggests that such models may not be truly predictive in spite of acceptable R 2 pred values. Another observation is that coefficient of determination R 2 for the training set is more immune to changes on deletion of variables than the validation parameters like Q 2 and R 2 pred . Finally, a new parameter r m 2 has been suggested to indicate external predictability of QSAR models.

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“…The value indicating the contribution of each predictor variable to a model is evaluated to decide on the (un)importance of the variables. A feature with an average value of the squared VIP scores close to or above 1 can be considered important in a model (181,182).…”

Section: Wrapper Methodsmentioning

confidence: 99%

Feature Selection Methods in QSAR Studies

Goodarzi

Dejaegher

Heyden

2012

Journal of AOAC INTERNATIONAL

125

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A quantitative structure-activity relationship (QSAR) relates quantitative chemical structure attributes (molecular descriptors) to a biological activity. QSAR studies have now become attractive in drug discovery and development because their application can save substantial time and human resources. Several parameters are important in the prediction ability of a QSAR model. On the one hand, different statistical methods may be applied to check the linear or nonlinear behavior of a data set. On the other hand, feature selection techniques are applied to decrease the model complexity, to decrease the overfitting/overtraining risk, and to select the most important descriptors from the often more than 1000 calculated. The selected descriptors are then linked to a biological activity of the corresponding compound by means of a mathematical model. Different modeling techniques can be applied, some of which explicitly require a feature selection. A QSAR model can be useful in the design of new compounds with improved potency in the class under study. Only molecules with a predicted interesting activity will be synthesized. In the feature selection problem, a learning algorithm is faced with the problem of selecting a relevant subset of features upon which to focus attention, while ignoring the rest. Up to now, many feature selection techniques, such as genetic algorithms, forward selection, backward elimination, stepwise regression, and simulated annealing have been used extensively. Swarm intelligence optimizations, such as ant colony optimization and partial swarm optimization, which are feature selection techniques usually simulated based on animal and insect life behavior to find the shortest path between a food source and their nests, recently are also involved in QSAR studies. This review paper provides an overview of different feature selection techniques applied in QSAR modeling.

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The Effect of Variable Selection on the Non‐linear Modelling of Oestrogen Receptor Binding

Cited by 11 publications

References 43 publications

Estimation of drug solubility in water, PEG 400 and their binary mixtures using the molecular structures of solutes

Estimation of drug solubility in water, PEG 400 and their binary mixtures using the molecular structures of solutes

On Some Aspects of Variable Selection for Partial Least Squares Regression Models

Feature Selection Methods in QSAR Studies

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