Quantitative structure–retention relationships (QSRR) models were constructed for the GC relative retention times (RRTs) of 126 polybrominated diphenyl ether (PBDE) congeners. First, a number of topological and connectivity indices descriptors were derived from E-dragon software. In a further step, six molecular descriptors were extracted by genetic algorithm (GA) coupled with multiple linear regression (MLR) method. The QSRR model was established using a support vector machine (SVM) algorithm as regression tool. High training sets correlation coefficients R 2 indicated that >99.6 % (except for stationary phase CP-Sil 19) of the total variation in the predicted RRTs is explained by the fitted models. It showed that we provided a more accurate model that was subsequently used to predict the RRTs of validation sets. The excellent statistical parameters Q 2 loo (correlation coefficient of leave-one-out cross validation) and validation sets correlation coefficients R 2 > 99.0 % reveal that the models are robust and have high internal and external predictive capability. According to sum of ranking differences (SRD) validation values, we concluded that DB-1 and DB-5 are the best two models.