简介：Inthiswork,somechemometricsmethodsareappliedforthemodelingandpredictionoftheHildebrandsolubilityparameterofsomepolymers.Ageneticalgorithm(GA)methodisdesignedfortheselectionofvariablestoconstructtwomodelsusingthemultiplelinearregression(MLR)andleastsquare-supportvectormachine(LS-SVM)methodsinordertopredicttheHildebrandsolubilityparameter.TheMLRmethodisusedtobuildalinearrelationshipbetweenthemoleculardescriptorsandtheHildebrandsolubilityparameterforthesecompounds.ThentheLS-SVMmethodisutilizedtoconstructthenon-linearquantitativestructure-activityrelationship(QSAR)models.TheresultsobtainedusingtheLS-SVMmethodarethencomparedwiththoseobtainedfortheMLRmethod;itwasrevealedthattheLS-SVMmodelwasmuchbetterthantheMLRone.Theroot-mean-squareerrorsofthetrainingsetandthetestsetfortheLS-SVMmodelwere0.2912and0.2427,andthecorrelationcoefficientswere0.9662and0.9518,respectively.ThispaperprovidesanewandeffectivemethodforpredictingtheHildebrandsolubilityparameterforsomepolymers,andalsorevealsthattheLS-SVMmethodcanbeusedasapowerfulchemometricstoolforthequantitativestructure-propertyrelationship(QSPR)studies.