QSPR modeling of nonionic surfactant cloud points: An update

Quantitative structure–property relationship (QSPR) models for the cloud points of nonionic surfactants were developed based on CODESSA descriptors. Essentials accounting for a reliable model were considered carefully. Four descriptors were selected by a generic algorithm (GA) method to link the structures of nonionic surfactants to their corresponding cloud-point values. The descriptors were also analyzed using principal component analysis (PCA). Nonlinear models based on support vector machine (SVM) and projection pursuit regression (PPR) were also developed. All models were validated in two ways, i.e., internal cross-validation (CV) and a test set. The results are discussed in light of the main factors that influence the property under investigation and its modeling. In addition, an independent external data set of 16 nonionic surfactants was used to check the generalization ability of the optimum model.

The support vector machine (SVM) model is tested in the current study. The use of this SVM approach to predict properties of unknown surfactants has been checked with an independent external test set of 16 compounds. This figure shows a good correlation between predicted and reported cloud-point values.Figure optionsDownload high-quality image (69 K)Download as PowerPoint slideHighlights
► Support vector machine was used to investigate the cloud points of surfactants.
► Molecular structures were examined using principal component analysis.
► The optimum model was checked using an external set of 16 different compounds.
► Properties of 14 out of 16 external surfactants were predicted very well.