Quantitative structure–activity relationship (QSAR) study of interleukin-1 receptor associated kinase 4 (IRAK-4) inhibitor activity by the genetic algorithm and multiple linear regression (GA-MLR) method

In this study, the quantitative structure-activity relationship (QSAR) models for the superoxide quenching activity of fullerene derivatives were developed. The dataset was divided into a training set and test set, based on hierarchical clustering technique. Three variables were selected by the genetic algorithm (GA) variable subset selection procedure. Multiple linear regressions (MLR) and support vector machine (SVM) were used as linear and nonlinear methods. Both the linear and nonlinear models could give very satisfactory prediction results: The square correlation coefficient of R 2 for the training and test sets were 0.826 and 0.981, by MLR and 0.957 and 0.965, by SVM methods, respectively. The prediction result of the SVM model was better than that obtained by MLR method, which proved SVM was a useful tool in the prediction of the superoxide quenching activity of fullerene derivatives. The results suggested that the minimum atomic partial charge, minimum valence of hydrogen atom, and minimum atomic state energy of oxygen atom, are the main independent factors contributing to the superoxide quenching activity of fullerene derivatives.

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“…The details of Williams plot has been illustrated in our previous works (44)(45)(46). For this study, the William plot is shown in Figure 3.…”

Section: Resultsmentioning

confidence: 98%

Prediction of Superoxide Quenching Activity of Fullerene (C₆₀) Derivatives by Genetic Algorithm-Support Vector Machine

Pourbasheer

Aalizadeh

Norouzi

2014

Fullerenes, Nanotubes and Carbon Nanostructures

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“…34 As it is clear from Table I, G3e has a positive sign, illustrating a greater coefficient Copyright of Journal of the Serbian Chemical Society is the property of National Library of Serbia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. Weighted holistic invariant molecular (WHIM) descriptors are geometrical descriptors based on the statistical indices calculated on the projections of atoms along the principal axes.…”

Section: Interpretation Of Descriptorsmentioning

confidence: 99%

Application of genetic algorithm - multiple linear regressions to predict the activity of RSK inhibitors

Avval

Pourbashir

Norouzi

2015

JSCS

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This paper considers the development of a linear quantitative structure-activity relationship (QSAR) model for predicting the ribosomal S6 kinase (RSK) inhibition activity of some new compounds. A dataset consisting of 59 pyrazino[1,2-α]indole, diazepino[1,2-α]indole, and imidazole derivatives with known inhibitory activities was used. The multiple linear regressions (MLR) technique combined with stepwise (SW) and the genetic algorithm (GA) methods as variable selection tools was employed. For more checking of the stability, robustness and predictability of the proposed models, internal and external validation techniques were used. Comparison of the obtained results, indicate that the GA-MLR model is superior to the SW-MLR model and that it is applicable for designing novel RSK inhibitors.

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“…Recent application of MLR in QSAR or QSPR includes prediction for luteinizing hormone-releasing hormone antagonists [23], 5-HT6 receptor ligands [24], interleukin-1 receptor-associated kinase 4 inhibitors [25], potencies of endocrine disruptors [26], and chlorine demand by organic molecules [27]. Recent application of MLR in QSAR or QSPR includes prediction for luteinizing hormone-releasing hormone antagonists [23], 5-HT6 receptor ligands [24], interleukin-1 receptor-associated kinase 4 inhibitors [25], potencies of endocrine disruptors [26], and chlorine demand by organic molecules [27].…”

Section: Multiple Linear Regressionmentioning

confidence: 99%

Current Modeling Methods Used in QSAR/QSPR

Yee

Wei

2012

Statistical Modelling of Molecular Descriptors in QSAR/QSPR

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j1 diagnostic abilities. They can be used to predict the biological activity (e.g., IC 50 ) or class (e.g., inhibitor versus noninhibitors) of compounds before the actual biological testing. They can also be used in the analysis of structural characteristics that can give rise to the properties of interest.As illustrated in Figure 1.1, developing QSAR models starts with the collection of data for the property of interest while taking into consideration the quality of the data. It is necessary to exclude low-quality data as they will lower the quality of the model. Following that, representation of the collected molecules is done through the use of features, namely molecular descriptors, which describes important information of the molecules. There are many types of molecular descriptors but not all will be useful for a particular modeling task. Thus, uninformative or redundant molecular descriptors should be removed before the modeling process. Subsequently, for tuning and validation of the QSAR model, the full data set is divided into a training set and a testing set prior to learning.During the learning process, various modeling methods like multiple linear regression, logistic regression, and machine learning methods are used to build models that describe the empirical relationship between the structure and property of interest. The optimal model is obtained by searching for the optimal modeling parameters and feature subset simultaneously. This finalized model built from the optimal parameters will then undergo validation with a testing set to ensure that the model is appropriate and useful. Figure 1.1 General workflow of developing a QSAR model. 2j 1 Current Modeling Methods Used in QSAR/QSPR Figure 1.3 A simple structure showing the three layers of an artificial neural network. 6j 1 Current Modeling Methods Used in QSAR/QSPR Figure 1.4 GRNN is a neural network with four layers.

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Quantitative structure–activity relationship (QSAR) study of interleukin-1 receptor associated kinase 4 (IRAK-4) inhibitor activity by the genetic algorithm and multiple linear regression (GA-MLR) method

Cited by 42 publications

References 36 publications

Prediction of Superoxide Quenching Activity of Fullerene (C₆₀) Derivatives by Genetic Algorithm-Support Vector Machine

Prediction of Superoxide Quenching Activity of Fullerene (C₆₀) Derivatives by Genetic Algorithm-Support Vector Machine

Application of genetic algorithm - multiple linear regressions to predict the activity of RSK inhibitors

Current Modeling Methods Used in QSAR/QSPR

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Quantitative structure–activity relationship (QSAR) study of interleukin-1 receptor associated kinase 4 (IRAK-4) inhibitor activity by the genetic algorithm and multiple linear regression (GA-MLR) method

Cited by 42 publications

References 36 publications

Prediction of Superoxide Quenching Activity of Fullerene (C60) Derivatives by Genetic Algorithm-Support Vector Machine

Prediction of Superoxide Quenching Activity of Fullerene (C60) Derivatives by Genetic Algorithm-Support Vector Machine

Application of genetic algorithm - multiple linear regressions to predict the activity of RSK inhibitors

Current Modeling Methods Used in QSAR/QSPR

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Product

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Prediction of Superoxide Quenching Activity of Fullerene (C₆₀) Derivatives by Genetic Algorithm-Support Vector Machine

Prediction of Superoxide Quenching Activity of Fullerene (C₆₀) Derivatives by Genetic Algorithm-Support Vector Machine