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

• The genetic programming (GP) model accurately predicts the solubility parameters.
• The GP reconstruct transparent relationship to predict the solubility parameters.
• The GP captures nonlinear relationships among the molecular descriptors.

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 –(C1H2–C2R3R4)–, 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.