Application of Quantitative Structure-Property Relationship (QSPR) Models for the Predictions of Critical Micelle Concentration of Gemini Imidazolium Surfactants

Abstract

Abstract The critical micelle concentration (cmc) is important indexes to determine the performance of surfactants quantitatively. In this study, based on the molecular descriptors, which calculated, by Dragon software, were proposed a quantitative structure-property relationship analysis for prediction of cmc of gemini imidazolium surfactants. In the present study, the associative neural networks (ASNN) and multiple linear regression analysis (MLRA) technique were used to interpret the chemical structural functionality (molecular descriptors) against the cmc of gemini imidazolium surfactants. The models were validated rigorously through 5-fold cross-validation. The ASNN method showed to be better than the MLRA method in terms of the internal and the external prediction accuracy with high statistical quality r2 = 0.96, q2 = 0.95, RMSE = 0.08 and MAE = 0.04 respectively. The developed QSPR models are publicly available on the web site at https://ochem.eu/model/11583111 for ASNN model and https://ochem.eu/model/43858671 for MLRA model. These models can be applied to predict the cmc of new gemini imidazolium surfactants.