Two-step hybrid modeling for variable selection and estimation: An application to quantitative structure activity relationship study

Abstract

In this study, we developed a simple technique for effective parameter estimation and prediction of the quantitative structure activity relationship studies using a two-step procedure. The first step is to choose the important molecular descriptors using the random forest regression, and the second step is to optimally predict the biological activity of the selected chemical compounds using the following estimators: ridge regression, jackknife ridge, Liu regression, jackknife Liu, Kibria–Lukman, and jackknife Kibria–Lukman. We conducted a simulation study and a real-life analysis with a quantitative structure–activity relationship (QSAR) data with 2540 descriptors after preprocessing. The optimal prediction is determined using the cross-validation error. The estimator with minimum cross-validation error is considered best. It is obvious that performing jackknife estimation after random forest selection is preferred.