Combination of genetic algorithm and partial least squares for cloud point prediction of nonionic surfactants from molecular structures.
Quantitative structure-property relationship (QSPR) analysis has been directed to a series of pure nonionic surfactants containing linear alkyl, cyclic alkyl, and alkey phenyl ethoxylates. Modeling of cloud point of these compounds as a function of the theoretically derived descriptors was established by multiple linear regression (MLR) and partial least squares (PLS) regression. In this study, a genetic algorithm (GA) was applied as a variable selection method in QSPR analysis. The results indicate that the GA is a very effective variable selection approach for QSPR analysis. The comparison of the two regression methods used showed that PLS has better prediction ability than MLR.
Ghasemi
Jahanbakhsh
J
Chemistry Department, Faculty of Sciences, Razi University, Kermanshah, Iran. jahan.ghasemi@gmail.com
Ahmadi
Shahin
S
D000465:Algorithms; D016018:Least-Squares Analysis; D008956:Models, Chemical; D015394:Molecular Structure; D044367:Phase Transition; D021281:Quantitative Structure-Activity Relationship; D012044:Regression Analysis; D013501:Surface-Active Agents / Q000737:chemistry*