QSPR Studies for Solubility Parameter by Means of Genetic Algorithm‐Based Multivariate Linear Regression and Generalized Regression Neural Network

Gharagheizi, Farhad

doi:10.1002/qsar.200630159

Cited by 71 publications

(108 citation statements)

References 14 publications

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“…Other recent articles present a similar two-phase methodology [27,28]. In one of these works [27], the subsets of descriptors selected by a genetic algorithm are then used by a neural network model.…”

Section: Related Workmentioning

confidence: 99%

“…In one of these works [27], the subsets of descriptors selected by a genetic algorithm are then used by a neural network model. Unlike our approach, this sec-ond phase is not part of the FS process.…”

Section: Related Workmentioning

confidence: 99%

“…The choice for these four predictors was based on their use in previous works on FS [10,27,37,38]. A discarding of linear-correlated variables is applied prior to the application of the wrapper algorithm.…”

Section: Feature Subset Evaluationmentioning

confidence: 99%

“…For the logP data set, the good performance of the MLR is interesting, since it shows that MLR could be a very good method for identifying predictive capacity of the descriptors even when the relationship with the target variable is nonlinear [27].…”

Section: Analysis and Comparison Of The Different Mo Wrappersmentioning

confidence: 99%

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Multi‐Objective Feature Selection in QSAR Using a Machine Learning Approach

et al. 2009

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The selection of descriptor subsets for QSAR/QSPR is a hard combinatorial problem that requires the evaluation of complex relationships in order to assess the relevance of the selected subsets. In this paper, we describe the main issues in applying descriptor selection for QSAR methods and propose a novel two-phase methodology for this task. The first phase makes use of a multi-objective evolutionary technique which yields interesting advantages compared to mono-objective methods. The second phase complements the first one and it enables to refine and improve the confidence in the chosen subsets of descriptors. This methodology allows the selection of subsets when a large number of descriptors are involved and it is also suitable for linear and nonlinear QSAR/QSPR models. The proposed method was tested using three data sets with experimental values for blood-brain barrier penetration, human intestinal absorption and hydrophobicity. Results reveal the capability of the method for achieving subsets of descriptors with a high predictive capacity and a low cardinality. Therefore, our proposal constitutes a new promising technique helpful for the development of QSAR/QSPR models.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Feature Subset Evaluationmentioning

confidence: 99%

Section: Analysis and Comparison Of The Different Mo Wrappersmentioning

confidence: 99%

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Multi‐Objective Feature Selection in QSAR Using a Machine Learning Approach

et al. 2009

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“…Then, the descriptors that were most effective for the prediction of critical pressure were selected through forward selection regression and genetic algorithm. This kind of descriptor selection procedure has been utilized in other studies regarding QSPR models (Wang et al, 2006;Gharagheizi, 2008), but few of these studies addressed why the descriptors were chosen. Our research includes an analysis of the implications of some selected descriptors and the relationship between these descriptors and critical pressure data.…”

Section: Introductionmentioning

confidence: 99%

A Quantitative Structure-Property Relationship Model for Predicting the Critical Pressures of Organic Compounds Containing Oxygen, Sulfur, and Nitrogen

Park

Kim³

et al. 2017

J. Chem. Eng. Japan / JCEJ

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In the present research, the critical pressures of organic compounds were selected as a model case and were predicted using a quantitative structure-property relationship model. The coverage of prediction contains hydrocarbons and nonhydrocarbon organic compounds containing O, S, and N atoms. In total, 802 hydrocarbons and 1144 non-hydrocarbon organic compounds were used to develop a model with the 3D structure of each compound being optimized by quantum mechanical calculations. Furthermore, appropriate descriptors to explain critical pressure e ectively were selected by forward selection regression and genetic algorithm. Multi-linear regression and neural networks were used to establish prediction models for the hydrocarbon and non-hydrocarbon organic compounds. The prediction models achieved su ciently high performances, with R 2 >0.96. This research also analyzes implications of selected descriptors, and the relationship between the descriptor and critical pressure.

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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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QSPR Studies for Solubility Parameter by Means of Genetic Algorithm‐Based Multivariate Linear Regression and Generalized Regression Neural Network

Cited by 71 publications

References 14 publications

Multi‐Objective Feature Selection in QSAR Using a Machine Learning Approach

Multi‐Objective Feature Selection in QSAR Using a Machine Learning Approach

A Quantitative Structure-Property Relationship Model for Predicting the Critical Pressures of Organic Compounds Containing Oxygen, Sulfur, and Nitrogen

Current Modeling Methods Used in QSAR/QSPR

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