Performance of multicomponent self-organizing regression (MCSOR) in QSAR, QSPR, and multivariate calibration: comparison with partial least-squares (PLS) and validation with large external data sets.
A novel method for underdetermined regression problems, multicomponent self-organizing regression (MCSOR), has been recently introduced. Here, its performance is compared with partial least-squares (PLS), which is perhaps the most widely adopted multivariate method in chemometrics. A potpourri of models is presented, and MCSOR appears to provide highly predictive models that are comparable with or better than the corresponding PLS models in large internal (leave-one-out, LOO) and pseudo-external (leave-many-out, LMO) validation tests. The "blind" external predictive ability of MCSOR and PLS is demonstrated employing large melting point, factor Xa, log P and log S data sets. In a nutshell, MCSOR is fast, conceptually simple (employing multiple linear regression, MLR, as a statistical tool), and applicable to all kinds of multivariate problems with single Y-variable.
Tuppurainen
K
K
Department of Chemistry, University of Kuopio, PO Box 1627, Kuopio, Finland. Kari.Tuppurainen@uku.fi
Korhonen
S-P
SP
Ruuskanen
J
J
D000465:Algorithms; D002138:Calibration; D002621:Chemistry / Q000379:methods; D002626:Chemistry, Pharmaceutical / Q000379:methods; D003627:Data Interpretation, Statistical; D004345:Drug Industry / Q000379:methods; D015233:Models, Statistical; D008962:Models, Theoretical; D015999:Multivariate Analysis; D021281:Quantitative Structure-Activity Relationship; D012044:Regression Analysis; D012984:Software; D013256:Steroids / Q000737:chemistry
D013256:Steroids