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Prediction of soil sorption coefficients with a conductor‐like screening model for real solvents
Author(s) -
Klamt Andreas,
Eckert Frank,
Diedenhofen Michael
Publication year - 2002
Publication title -
environmental toxicology and chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.1
H-Index - 171
eISSN - 1552-8618
pISSN - 0730-7268
DOI - 10.1002/etc.5620211206
Subject(s) - logarithm , sorption , partition coefficient , mathematics , standard deviation , mean squared error , statistics , moment (physics) , quantum chemical , set (abstract data type) , test set , quantitative structure–activity relationship , chemistry , computer science , chromatography , physics , adsorption , mathematical analysis , molecule , organic chemistry , quantum mechanics , programming language , stereochemistry
Using a general theory for partition coefficients based on a quantum chemically derived conductor‐like screening model for real solvents σ‐moment descriptors, the logarithmic soil sorption coefficients log K OC of a database of 440 compounds has been successfully correlated, achieving a standard deviation (root‐means‐squared [RMS]) of 0.62 log‐units on the training set and a predictive RMS of 0.72 log‐units on a more demanding test set. The quality of this generally applicable predictive approach is almost the same as that of a regression of log K OC with experimental log K OW values, which are the best correlations currently available. The error of this new predictive method is only approximately 43% of the error of a recently published model using a different quantum chemically based approach.