Accurate Quantitative Structure−Property Relationship Model To Predict the Solubility of C60in Various Solvents Based on a Novel Approach Using a Least-Squares Support Vector Machine

2005 ◽  
Vol 109 (43) ◽  
pp. 20565-20571 ◽  
Author(s):  
Huanxiang Liu ◽  
Xiaojun Yao ◽  
Ruisheng Zhang ◽  
Mancang Liu ◽  
Zhide Hu ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Support Vector ◽  
Quantitative Structure ◽  
Structure Property ◽  
Quantitative Structure Property Relationship ◽  
Property Relationship ◽  
Novel Approach ◽  
Structure Property Relationship ◽  
Relationship Model

scholarly journals CMC of diverse Gemini surfactants modelling using a hybrid approach combining SVR-DA

2020 ◽  
pp. 48-48
Author(s):  
Maamar Laidi ◽  
Abdallah Abdallah el Hadj ◽  
Cherif Si-Moussa ◽  
Othmane Benkortebi ◽  
Mohamed Hentabli ◽  
...  
Keyword(s):  
Least Squares ◽  
Support Vector Regression ◽  
Gemini Surfactants ◽  
Hybrid Approach ◽  
Support Vector ◽  
Quantitative Structure ◽  
Structure Property ◽  
Quantitative Structure Property Relationship ◽  
Property Relationship ◽  
Structure Property Relationship

As quantitative structure-property relationship (QSPR) technique provides a suitable tool to predict the critical micelle concentration (CMC) of Gemini surfactants from their structure descriptors. In this study, a comparative work was conducted to model the CMC property of 211 diverse Gemini surfactants based on their structural characteristics using linear and non-linear quantitative structure-property relationship models. Least squares model (OLS) and partial least squares (PLS) against k-nearest neighbours regression model (KNN), artificial neural network (ANN) and support vector regression (SVR) have been developed to model the CMC. Molecular descriptors were calculated and screened to remove unsuitable descriptors and improve the learning. Results indicate that the improved performance of support vector regression when the hyper-parameters are optimized using Dragonfly algorithm (SVR-DA) was highly capable of predicting the pCMC (-logCMC) values with an average absolute relative deviation (AARD) of 0.666 and coefficient of determination (R?) of 0.9971 for the global dataset.

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