A genetic programming-based QSPR model for predicting solubility parameters of polymers

作者：Koc Dilek Imren^*; Koc Mehmet Levent

来源：Chemometrics and Intelligent Laboratory Systems, 2015, 144: 122-127.

DOI：10.1016/j.chemolab.2015.04.005

• Solubility parameter
• Genetic programming
• Polymers
• Linear regression
• QSPR

In this study, linear and nonlinear quantitative structure-property relationship (QSPR) models, respectively called the multiple linear regression based QSPR (MLR-QSPR) model and the genetic programming based QSPR (GP-QSPR) model, were built to predict the solubility parameters of polymers with structure -((CH2)-H-1-(CRR4)-R-2-R-3)-, as function of some constitutional, topological and quantum chemical descriptors. The results from the internal validation analysis indicated that the GP-QSPR model has better goodness of fit statistics. The external and overall validation measures also confirmed that the GP-QSPR model significantly outperforms the MLR-QSPR model in terms of some performance metrics over the same testing data set, and that genetic programming has good potential to obtain more accurate models in QSPR studies.

• 出版日期2015-5-15