Quantitative structure-property relationship studies on electrochemical degradation of substituted phenols using a support vector machine

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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Quantitative Structure-Property Relationship Studies for Predicting Flash Points of Organic Compounds using Support Vector Machines

QSAR & Combinatorial Science ◽

10.1002/qsar.200810009 ◽

2008 ◽

Vol 27 (8) ◽

pp. 1013-1019 ◽

Cited By ~ 23

Author(s):

Yong Pan ◽

Juncheng Jiang ◽

Rui Wang ◽

Hongyin Cao ◽

Jinbo Zhao

Keyword(s):

Support Vector Machines ◽

Organic Compounds ◽

Support Vector ◽

Quantitative Structure ◽

Structure Property ◽

Quantitative Structure Property Relationship ◽

Property Relationship ◽

Vector Machines ◽

Structure Property Relationship

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Artificial Neural Network and Support Vector Regression Applied in Quantitative Structure-property Relationship Modelling of Solubility of Solid Solutes in Supercritical CO2

Kemija u industriji ◽

10.15255/kui.2020.004 ◽

2020 ◽

Vol 69 (11-12) ◽

pp. 611-630

Author(s):

Mohammed Moussaoui ◽

Maamar Laidi ◽

Salah Hanini ◽

Mohamed Hentabli

Keyword(s):

Support Vector ◽

Quantitative Structure ◽

Structure Property ◽

Qspr Model ◽

Quantitative Structure Property Relationship ◽

Qsar Studies ◽

Property Relationship ◽

Proposed Model ◽

Structure Property Relationship ◽

Better Than

In this study, the solubility of 145 solid solutes in supercritical CO2 (scCO2) was correlated using computational intelligence techniques based on Quantitative Structure-Property Relationship (QSPR) models. A database of 3637 solubility values has been collected from previously published papers. Dragon software was used to calculate molecular descriptors of 145 solid systems. The genetic algorithm (GA) was implemented to optimise the subset of the significantly contributed descriptors. The overall average absolute relative deviation MAARD of about 1.345 % between experimental and calculated values by support vector regress SVR-QSPR model was obtained to predict the solubility of 145 solid solutes in supercritical CO2, which is better than that obtained using ANN-QSPR model of 2.772 %. The results show that the developed SVR-QSPR model is more accurate and can be used as an alternative powerful modelling tool for QSAR studies of the solubility of solid solutes in supercritical carbon dioxide (scCO2). The accuracy of the proposed model was evaluated using statistical analysis by comparing the results with other models reported in the literature.

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Quantitative Structure- Property Relationship (QSPR) Investigation of Camptothecin Drugs Derivatives

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207321666180927102836 ◽

2018 ◽

Vol 21 (7) ◽

pp. 533-542 ◽

Cited By ~ 2

Author(s):

Neda Ahmadinejad ◽

Fatemeh Shafiei ◽

Tahereh Momeni Isfahani

Keyword(s):

Thermodynamic Properties ◽

Predictive Ability ◽

Total Sample ◽

Basis Sets ◽

Quantitative Structure ◽

Structure Property ◽

Quantitative Structure Property Relationship ◽

Chemical Structures ◽

Property Relationship ◽

Structure Property Relationship

Aim and Objective: Quantitative Structure- Property Relationship (QSPR) has been widely developed to derive a correlation between chemical structures of molecules to their known properties. In this study, QSPR models have been developed for modeling and predicting thermodynamic properties of 76 camptothecin derivatives using molecular descriptors. Materials and Methods: Thermodynamic properties of camptothecin such as the thermal energy, entropy and heat capacity were calculated at Hartree–Fock level of theory and 3-21G basis sets by Gaussian 09. Results: The appropriate descriptors for the studied properties are computed and optimized by the genetic algorithms (GA) and multiple linear regressions (MLR) method among the descriptors derived from the Dragon software. Leave-One-Out Cross-Validation (LOOCV) is used to evaluate predictive models by partitioning the total sample into training and test sets. Conclusion: The predictive ability of the models was found to be satisfactory and could be used for predicting thermodynamic properties of camptothecin derivatives.

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