Liu, T., Öberg, T.
Journal of Chemometrics 23, 254-262 (2009).
Abstract
Estimation methods for partition constants are needed in many fields of
engineering and science. The partitioning between phases is determined by the
free energy of transfer and all estimation methods must therefore describe the
same entity. Linear solvation energy relationships (LSER) try to split the
contributions to van der Waals and polar interactions into directly
interpretable solute descriptors; while projection based regression methods can
accomplish a similar dimensionality reduction from a set of theoretical
descriptors. Here we use the partitioning between octanol and water (Kow)
and water solubility (Sw) to investigate similarities and
differences between LSER and partial least squares regression (PLSR) models. The
similarities in model structure are described, and shown to transform into a
comparable prediction performance. We also demonstrate the opportunity to
accomplish an analogous chemical interpretation of a PLSR model - either
directly or through a linear transformation of the PLS factors - as with a LSER
model. Much of the alleged difference between the mechanistic or semi-empirical
LSER and the statistical PLSR models will then disappear. The choice of a
modelling approach should therefore primarily be driven by the availability of
data and predictive performance.
Postprint
as a PDF-fil, 116 kb
DOI: 10.1002/cem.1224
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