Octanol-water partition: Searching for predictive models

Peter Buchwald, Nicholas Bodor

Research output: Contribution to journalReview articlepeer-review

155 Scopus citations


The log n-octanol/water partition coefficient (log P(o/w)) still represents one of the most informative physicochemical parameters available to medicinal chemists. In the present work, principles, methodologies, and parameters are briefly reviewed for a variety of models developed to predict this parameter based on molecular structure. To include the developments of recent years, a total of more than 40 different approaches are mentioned with relevant bibliography within four major categories: group contribution methods, atomic contribution methods, molecular methods, and other physicochemical methods. To underscore once more the utility of this partition coefficient, a comprehensive and reevaluated correlation between log P(o/w) and in vivo permeability data of rat brain capillaries is included. Most deviants that fell below the trendline are those that have been recently found to be substrates for P-glycoprotein, a multidrug transporter that actively removes them from the brain. Accurate predictions of log P(o/w) may necessitate many parameters, but there is mounting evidence that molecular size and hydrogen bonding ability can account for a major part of the variance. Our recently developed, molecular size-based approach is reviewed, and it is argued that introduction of three-dimensionality allows the elimination of many empirically derived fragment constants without a significant deterioration of the predictive accuracy. A comparison of predictive power for six different methods on 145 molecules of interest for medicinal chemists is also included.

Original languageEnglish (US)
Pages (from-to)353-380
Number of pages28
JournalCurrent medicinal chemistry
Issue number5
StatePublished - Oct 26 1998
Externally publishedYes

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Medicine
  • Pharmacology
  • Drug Discovery
  • Organic Chemistry


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