LogP is a number that tells you how much a chemical compound prefers oil over water. Formally called the partition coefficient, it’s calculated by dissolving a substance in a mixture of water and a specific oil (1-octanol), letting the two layers separate, then measuring how much of the substance ends up in each layer. The result is expressed as a logarithm: a positive LogP means the compound favors the oily layer, while a negative LogP means it favors water. This single number turns out to be remarkably useful in drug design, environmental science, and toxicology.
The Math Behind LogP
LogP is the base-10 logarithm of the ratio of a compound’s concentration in octanol to its concentration in water, measured at equilibrium. In equation form: LogP = log₁₀(concentration in octanol / concentration in water). The octanol phase is saturated with water, and the water phase is saturated with octanol, which mimics the way biological membranes interact with surrounding fluids.
Because it uses a logarithmic scale, each whole number represents a tenfold difference. A compound with a LogP of 3 is ten times more concentrated in the octanol layer than one with a LogP of 2, and a hundred times more than one with a LogP of 1. A LogP of 0 means the compound splits equally between both layers.
Why Octanol and Water
Octanol was chosen as the standard organic solvent because it behaves surprisingly like a cell membrane. It has a long fatty chain (hydrophobic) with an alcohol group at one end (hydrophilic), so it mimics the mix of oily and polar regions found in biological tissues. This makes octanol-water partitioning a practical stand-in for how a molecule will behave when it encounters living cells, soil, or sediment. The octanol-water partition coefficient, sometimes written as KOW, has become one of the most widely used properties in chemistry and pharmacology.
LogP vs. LogD
LogP describes the partitioning of a compound’s neutral, unionized form only. It does not change with pH. Many drugs and chemicals, however, carry ionizable groups (like carboxylic acids or amines) that gain or lose a charge depending on the acidity of their environment. A charged molecule is far more water-soluble than its neutral counterpart, so the real-world distribution between oil and water shifts with pH.
LogD, the distribution coefficient, accounts for this. It measures the ratio of all forms of a compound (charged and uncharged combined) in octanol versus water at a specific pH. For a drug like ibuprofen, which contains a carboxylic acid group, LogD is high at low pH (where the acid is uncharged and lipophilic) and drops significantly at higher pH as the molecule ionizes and becomes more water-soluble. When scientists talk about how a drug actually behaves in the body, LogD at physiological pH (around 7.4) is often more informative than LogP alone.
Why LogP Matters in Drug Design
A drug molecule needs to be oily enough to cross cell membranes but water-soluble enough to dissolve in blood and reach its target. LogP captures that tension in a single number, which is why it’s one of the first properties medicinal chemists evaluate.
Lipinski’s Rule of Five, one of the most cited guidelines in pharmaceutical research, sets an upper LogP threshold of 5 for orally administered drugs. Compounds above that cutoff tend to be too greasy: they dissolve poorly in the gut, bind indiscriminately to proteins throughout the body, and accumulate in fatty tissues. This lack of selectivity can lead to off-target side effects and increased toxicity. The rule also flags compounds with molecular weights above 500 daltons and excessive numbers of hydrogen bond donors or acceptors.
For drugs that need to reach the brain, the window narrows considerably. Research on marketed central nervous system drugs found that blood-brain barrier penetration is optimal when LogP falls between roughly 1.5 and 2.7, with a mean around 2.1. The average calculated LogP for approved CNS drugs is 2.5, closely matching that range. Too lipophilic and a compound gets trapped in peripheral fat; too hydrophilic and it bounces off the tightly sealed blood vessels surrounding the brain.
LogP and Toxicity
High LogP values raise red flags for specific safety concerns. Compounds that are very lipophilic tend to have high membrane affinity, which means they can insert themselves into ion channels in heart muscle cells. Blocking these channels, particularly the hERG potassium channel, can disrupt the heart’s electrical rhythm and cause dangerous arrhythmias. Studies have found that LogP correlates with hERG inhibition better than LogD does, suggesting that the intrinsic greasiness of the neutral molecule is the primary driver of this risk. Positively charged, highly lipophilic compounds are especially prone to this problem.
Hepatotoxicity (liver damage) follows a similar pattern. Very lipophilic molecules tend to accumulate in the liver, where they undergo extensive metabolism that can produce reactive, toxic byproducts.
LogP in Environmental Science
Outside of drug development, LogP (or KOW) is a cornerstone of environmental risk assessment. A chemical’s tendency to partition into oily versus watery environments predicts several things: how strongly it sticks to soil particles, how readily it accumulates in the fatty tissues of fish and wildlife, and how persistent it is likely to be in aquatic ecosystems. Regulatory guidance documents around the world use KOW-based models to estimate bioconcentration factors, soil sorption coefficients, and baseline aquatic toxicity. Chemicals with very high LogP values, like many pesticides and industrial pollutants, tend to bioaccumulate up the food chain precisely because organisms absorb them into fat faster than they can eliminate them.
How LogP Is Measured
The classic approach is the shake-flask method. You dissolve the compound in a pre-equilibrated mixture of octanol and water, shake it, let the layers separate, then measure the concentration in each phase. It’s straightforward and reliable for compounds with LogP values between roughly -2 and 4, but it’s slow, requires relatively large amounts of pure material, and becomes unreliable for very lipophilic compounds (LogP above 4) because stubborn emulsions form between the two layers.
An alternative is the HPLC method, which works by running a compound through a chromatography column packed with a hydrophobic material. How long the compound takes to pass through the column correlates with its lipophilicity. Researchers calibrate the system using compounds whose LogP values are already known, then estimate unknown values from the calibration curve. This method covers a wider range (LogP 0 to 6) and uses far less material, but it can be inaccurate for charged molecules because their retention on the column involves more complex interactions than simple partitioning.
Calculated vs. Measured LogP
In practice, many researchers never measure LogP experimentally. They estimate it computationally using software that breaks a molecule into fragments and sums up each fragment’s known contribution to lipophilicity. The most common shorthand for this is cLogP (calculated LogP). Programs like ClogP, ACD/LogP, and KowWin all take this approach, with ClogP generally considered the most accurate predictor when compared against experimental data across large sets of compounds. These computational tools are fast enough to screen millions of candidate molecules in a drug discovery pipeline or flag environmental contaminants for further testing, making them indispensable in both fields.
Computational predictions do have limits. They can struggle with unusual functional groups, large flexible molecules, or compounds that form internal hydrogen bonds that aren’t captured by simple fragment-addition schemes. For high-stakes decisions, like advancing a drug candidate into clinical trials, experimental measurement remains the gold standard.