Application of Genetic Programming (GP) in Prediction of Gas Chromatographic Retention Time of some Pesticides
Keywords:
quantitative structure–retention relationships, pesticide, retention time, multiple linear regression, genetic programmingAbstract
In this study, quantitative structure–retention relationship (QSRR) methodology was
employed for modeling of gas chromatographic retention time for 74 pesticides. Stepwise
multiple linear regression (SW-MLR) was used for the selection of most important
descriptors. Multiple linear regression (MLR) and genetic programming (GP) were utilized
to develop linear and symbolic regression equation models, respectively. Inspection to
statistical parameters of developed MLR and GP models indicates symbolic regression
equation via GP can be selected as the best fitted model. For this model, the square
correlation coefficients (R2) were 0.943 and 0.911, and the root-mean square errors (RMSE)
were 2.56 and 2.77 for the training and test sets, respectively. The built GP model was
assessed by leave one out cross-validation (Q2
cv = 0.79, SPRESS = 2.57) as well as external
validation. In addition, the result of sensitivity analysis of GP model suggest structural
features and polarity are important factors responsible for gas-chromatographic retention
time values of studied pesticides.