Öberg, T., Liu, T.
Presentation at SETAC Europe 19th Annual Meeting in Göteborg, May 31-June
4, 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 latent variable models based on partial least squares
regression (PLSR). The similarity in model structure is described, and shown to
transform into a comparable prediction performance. We also demonstrate the
opportunity to accomplish an analogous chemical interpretation of the latent
variable 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.
See also the paper: Modelling of partition constants: Linear solvation energy relationships or PLS regression?
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