High-dimensional quantitative structure-activity relationship modeling of influenza neuraminidase a/PR/8/34 (H1N1) inhibitors based on a two-stage adaptive penalized rank regression

Outliers in the biological activity variable or the heavy tailed distribution of the error are often encountered in practice. Under these circumstances, the quantittative structure-activity relationship (QSAR) model using multiple linear regression is not efficient. In this paper, a two-stage adaptive penalized rank regression is proposed for constructing a robust and efficient high-dimensional QSAR model of influenza virus neuraminidase A/PR/8/34 (H1N1) inhibitors. The results demonstrate the effectiveness of our proposed method in simultaneously estimating a robust QSAR model and selecting informative molecular descriptors. Furthermore, the results prove that the proposed method can significantly encourage the grouping effect. The proposed method, because of the high predictive ability and robustness, could be a useful method in high-dimensional QSAR modeling. Copyright (c) 2015 John Wiley & Sons, Ltd. This study deals with the problem of the presence of outliers in the biological activity variable or the heavy tailed distribution of the error. A two-stage adaptive penalized rank regression (SIS-AAPRR) is proposed for building a robust and efficient high-dimensional quantitative structure-activity relationship (QSAR) model of influenza virus neuraminidase A/PR/8/34 (H1N1) inhibitors. The results reveal that the SIS-AAPRR significantly outperforms multiple linear regression in terms of estimating the robust QSAR model, selecting informative descriptors, and encouraging the grouping effect.