title: QSAR modeling based on the bias/variance compromise: a harmonious and parsimonious approach.
abstract: Modeling quantitative structure-activity relationships (QSAR) is considered with an emphasis on prediction. An abundance of methods are available to develop such models. Using a harmonious approach that balances the bias and variance of predictions, the best calibration models are identified relative to the bias and variance criteria used. Criteria utilized to determine the adequacy of models are the root mean square error of calibration (RMSEC) and validation (RMSEV), respective R2 values, and the norm of the regression vector. QSAR data from the literature are used to demonstrate concepts. For these data sets and criteria used, it is suggested that models obtained by ridge regression (RR) are more harmonious and parsimonious than models obtained by partial least squares (PLS) and principal component regression (PCR) when the data is mean-centered. The most harmonious RR models have the best bias/variance tradeoff, reflected by the smallest RMSEC, RMSEV, and regression vector norms and the largest calibration and validation R2 values. The most parsimonious RR models have the smallest effective rank.
authors.affiliation: Department of Chemistry, Idaho State University, Pocatello, ID 83209, USA. kalijohn@isu.edu
authors.forename: John H
authors.initials: JH
authors.lastname: Kalivas
authors.forename: Joel B
authors.initials: JB
authors.lastname: Forrester
authors.forename: Heather A
authors.initials: HA
authors.lastname: Seipel
mesh_terms: D016018:Least-Squares Analysis; D008958:Models, Molecular; D021281:Quantitative Structure-Activity Relationship
references.citation: J Chem Inf Comput Sci. 2002 Jan-Feb;42(1):94-102
references.pmid: 11855972
references.citation: J Chem Inf Comput Sci. 2002 Nov-Dec;42(6):1347-57
references.pmid: 12444731
references.citation: J Mol Graph Model. 2003 Mar;21(5):391-419
references.pmid: 12543137
references.citation: Appl Spectrosc. 2003 Mar;57(3):309-16
references.pmid: 14658623