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Toward robust QSPR models: Synergistic utilization of robust regression and variable elimination
Author(s) -
Grohmann Rainer,
Schindler Torsten
Publication year - 2007
Publication title -
journal of computational chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.907
H-Index - 188
eISSN - 1096-987X
pISSN - 0192-8651
DOI - 10.1002/jcc.20831
Subject(s) - outlier , quantitative structure–activity relationship , regression analysis , robust regression , regression , linear regression , computer science , feature selection , regression diagnostic , variable (mathematics) , data mining , statistics , artificial intelligence , mathematics , machine learning , polynomial regression , mathematical analysis
Widely used regression approaches in modeling quantitative structure–property relationships, such as PLS regression, are highly susceptible to outlying observations that will impair the prognostic value of a model. Our aim is to compile homogeneous datasets as the basis for regression modeling by removing outlying compounds and applying variable selection. We investigate different approaches to create robust, outlier‐resistant regression models in the field of prediction of drug molecules' permeability. The objective is to join the strength of outlier detection and variable elimination increasing the predictive power of prognostic regression models. In conclusion, outlier detection is employed to identify multiple, homogeneous data subsets for regression modeling. © 2007 Wiley Periodicals, Inc. J Comput Chem 2008