In Silico Prediction of Critical Micelle Concentration (CMC) of Classic and Extended Anionic Surfactants from Their Molecular Structural Descriptors

Abstract

CMC is an important parameter for the characterization of surfactants. Compared to other properties, the CMC can be correlated with surfactants performance characteristics on an industrial scale. In this investigation, QSPR models were established to identify the relation between the molecular structures and the critical micelle concentration (CMC) of 50 anionic surfactants employing four molecular structural descriptors. Three regression methods were chosen in this work to develop robust predictive models, namely multilayer perceptron–artificial neural network (MLP/ANN), multiple linear regressions, and partial least square approach. To establish the reliability and the robustness of the developed QSPR models, all available validation strategies were adopted. The best results $$ \left( {\overline{{r_{m}^{2} }} = 0.87;Q_{\text{LOO}}^{2} = 0.93;Q_{F1}^{2} = 0.95;\Delta r_{m}^{2} = 0.15} \right) $$ were obtained for MLP/ANN with a 4-3-1 artificial neural network model trained with the Broyden–Fletcher–Goldfarb–Shanno algorithm. In this study, it is observed that electronic properties, structure and size of the molecule, as well as the number of atoms in the longest aliphatic chain play major roles in the development of the CMC model of anionic surfactants.