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Octanol-water partition coefficients (log P) are key parameters for estimating toxicological and pharmacological properties of organic compounds. The significance of log P and the difficulty in experimentally measuring log P has led to the development of several Log P estimation techniques. Methods based on group contributions, such as CLOGP and CHEMICALC2, are widely used. While very fast, their reliability is highly dependent on the availability of experimental log P values for representative compounds. Quantitative structureactivity relationships (QSARs) have also been used to estimate log P and address the limitations of other methods. While QSARs also depend on a set of compounds with reliable log P values, use of general structural descriptors rather than specific fragments promises to provide more broadly applicable predictive models. A number of QSARs have been reported for predicting log P values, however these are limited to specific compound classes. This paper presents an approach to develop a broadly applicable QSAR for log P based on general molecular structural parameters. Examples are given for a variety of compound classes including those containing mixed functional groups. Emphasis is placed on compounds relevant to the household and personal products industries. Limitations and pitfalls in developing reliable predictive correlations are identified and strategies for extending the model are discussed.
Hydrophobicity, Octanol-Water Partition Coefficients, Log P, Surfactants, QSAR
Senior Group Leader, Unilever Research U.S., Edgewater, NJ
Principal Research Chemist, Unilever Research U.S., Edgewater, NJ