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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
Author(s) -
Algamal Zakariya Yahya,
Lee Muhammad Hisyam,
AlFakih Abdo Mohammed
Publication year - 2016
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.2766
Subject(s) - quantitative structure–activity relationship , robustness (evolution) , outlier , regression , linear regression , neuraminidase , computer science , rank (graph theory) , regression analysis , mathematics , data mining , artificial intelligence , machine learning , statistics , chemistry , biology , virus , biochemistry , virology , combinatorics , gene
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 © 2015 John Wiley & Sons, Ltd.