faster method of log p determination makes use of high-performance liquid chromatography. the log p of a solute can be determined by correlating its retention time with similar compounds with known log p values. an advantage of this method is that it is fast (5–20 minutes per sample). however, since the value of log p is determined by linear regression, several compounds with similar structures must have known log p values, and extrapolation from one chemical class to another—applying a regression equation derived from one chemical class to a second one—may not be reliable, since each chemical classes will have its characteristic regression parameters. ph-metric the ph-metric set of techniques determine lipophilicity ph profiles directly from a single acid-base titration in a two-phase water–organic-solvent system.: 280–4 hence, a single experiment can be used to measure the logarithms of the partition coefficient (log p) giving the distribution of molecules that are primarily neutral in charge, as well as the distribution coefficient (log d) of all forms of the molecule over a ph range, e.g., between 2 and 12. the method does, however, require the separate determination of the pka value(s) of the substance. electrochemical polarized liquid interfaces have been used to examine the thermodynamics and kinetics of the transfer of charged species from one phase to another. two main methods exist. the first is ities, "interfaces between two immiscible electrolyte solutions". the second is droplet experiments. here a reaction at a triple interface between a conductive solid, droplets of a redox active liquid phase and an electrolyte solution have been used to determine the energy required to transfer a charged species across the interface. single-cell approach there are attempts to provide partition coefficients for drugs at a single-cell level. this strategy requires methods for the determination of concentrations in individual cells, i.e., with fluorescence correlation spectroscopy or quantitative image analysis. partition coefficient at a single-cell level provides information on cellular uptake mechanism. prediction there are many situations where prediction of partition coefficients prior to experimental measurement is useful. for example, tens of thousands of industrially manufactured chemicals are in common use, but only a small fraction have undergone rigorous toxicological evaluation. hence there is a need to prioritize the remainder for testing. qsar equations, which in turn are based on calculated partition coefficients, can be used to provide toxicity estimates. calculated partition coefficients are also widely used in drug discovery to optimize screening libraries and to predict druglikeness of designed drug candidates before they are synthesized. as discussed in more detail below, estimates of partition coefficients can be made using a variety of methods, including fragment-based, atom-based, and knowledge-based that rely solely on knowledge of the structure of the chemical. other prediction methods rely on other experimental measurements such as solubility. the methods also differ in accuracy and whether they can be applied to all molecules, or only ones similar to molecules already studied. atom-based standard approaches of this type, using atomic contributions, have been named by those formulating them with a prefix letter: alogp, xlogp, mlogp, etc. a conventional method for predicting log p through this type of method is to parameterize the distribution coefficient contributions of various atoms to the overall molecular partition coefficient, which produces a parametric model. this parametric model can be estimated using constrained least-squares estimation, using a training set of compounds with experimentally measured partition coefficients. in order to get reasonable correlations, the most common elements contained in drugs (hydrogen, carbon, oxygen, sulfur, nitrogen, and halogens) are divided into several different atom types depending on the environment of the atom within the molecule. while this method is generally the least accurate, the advantage is that it is the most general, being able to provide at least a rough estimate for a wide variety of molecules. fragment-based the most common of these uses a group contribution method and is termed clogp. it has been shown that the log p of a compound can be determined by the sum of its non-overlapping molecular fragments (defined as one or more atoms covalently bound to each other within the molecule). fragmentary log p values have been determined in a statistical method analogous to the atomic methods (least-squares fitting to a training set). in addition, hammett-type corrections are included to account of electronic and steric effects. this method in general gives better results than atomic-based methods, but cannot be used to predict partition coefficients for molecules containing unusual functional groups for which the method has not yet been parameterized (most likely because of the lack of experimental data for molecules containing such functional groups).: 125ff : 1–193 knowledge-based a typical data-mining-based prediction uses support-vector machines, decision trees, or neural networks. this method is usually very successful for calculating log p values when used with compounds that have similar chemical structures and known log p values. molecule mining approaches apply a similarity-matrix-based prediction or an automatic fragmentation scheme into molecular substructures. furthermore, there exist also approaches using maximum common subgraph searches or molecule kernels. log d from log p and pka for cases where the molecule is un-ionized: log ⁡ d ≅ log ⁡ p . for other cases, estimation of log d at a given ph, from log p and the known mole fraction of the un-ionized form, f 0 , in the case where partition of ionized forms into non-polar phase can be neglected, can be formulated as log ⁡ d ≅ log ⁡ p + log ⁡ ( f 0 ) . \right).} the following approximate expressions are valid only for monoprotic acids and bases: log ⁡ d acids ≅ log ⁡ p + log ⁡ [ 1 1 + 10 p h − p k a ] , log ⁡ d bases ≅ log ⁡ p + log ⁡ [ 1 1 + 10 p k a − p h ] . \log d_&\cong \log p+\log \left,\\\log d_&\cong \log p+\log \left.\end further approximations for when the compound is largely ionized: for acids with p h − p k a > 1 -\mathrm k_>1} , log ⁡ d acids ≅ log ⁡ p + p k a − p h \cong \log p+\mathrm k_-\mathrm } , for bases with p k a − p h > 1 k_-\mathrm >1} , log ⁡ d bases ≅ log ⁡ p − p k a + p h \cong \log p-\mathrm k_+\mathrm } . for prediction of pka, which in turn can be used to estimate log d, hammett type equations have frequently been applied. log p from log s if the solubility, s, of an organic compound is known or predicted in both water and 1-octanol, then log p can be estimated as log ⁡ p = log ⁡ s o − log ⁡ s w . -\log s_.} there are a variety of approaches to predict solubilities, and so log s. octanol-water partition coefficient the partition coefficient between n-octanol and water is known as the n-octanol-water partition coefficient , or kow. it is also frequently referred to by the symbol p, especially in the english literature. it is also known as n-octanol-water partition ratio. kow, being a type of partition coefficient, serves as a measure of the relationship between lipophilicity (fat solubility) and hydrophilicity (water solubility) of a substance. the value is greater than one if a substance is more soluble in fat-like solvents such as n-octanol, and less than one if it is more soluble in water. example values values for log kow typically range between -3 (very hydrophilic) and +10 (extremely lipophilic/hydrophobic). the values listed here are sorted by the partition coefficient. acetamide is hydrophilic, and 2,2′,4,4′,5-pentachlorobiphenyl is lipophilic. substance log kow t reference acetamide −1.155 25 °c methanol −0.824 19 °c formic acid −0.413 25 °c diethyl ether 0.833 20 °c p-dichlorobenzene 3.370 25 °c hexamethylbenzene 4.610 25 °c 2,2′,4,4′,5-pentachlorobiphenyl 6.410 ambient