Formula Used
The calculation of the partition coefficient (LogP) relies on the summation of individual atomic or fragment contributions ($a_i$):
$$LogP = \sum_{i} a_i$$
For ionizable compounds, the distribution coefficient (LogD) at a specific pH is evaluated using the Henderson-Hasselbalch equation adjusted for dissociation:
$$LogD_{acid} = LogP - \log_{10}\left(1 + 10^{pH - pKa}\right)$$
$$LogD_{base} = LogP - \log_{10}\left(1 + 10^{pKa - pH}\right)$$
How to Use This Calculator
- Input the valid molecular structure string using standard SMILES format into the designated text field.
- Select your preferred estimation method, such as Crippen Descriptors or Neural Network QSAR models.
- Specify environmental variables including target pH levels, operational temperature, and compound ionization class.
- Click the calculate button to instantly review your computed partition and distribution coefficients.
Comprehensive Guide to LogP and LogD in Drug Discovery
Understanding lipophilicity is fundamental in modern medicinal chemistry, pharmacology, and environmental science. Lipophilicity describes a chemical compound's ability to dissolve in fats, oils, lipids, and non-polar solvents. Two core metrics quantify this chemical property: LogP and LogD. While LogP measures the partition coefficient of a neutral, uncharged species between an octanol and water phase, LogD represents the distribution coefficient of all species—both ionized and neutral—at a specific physiological pH.
In pharmaceutical research, these parameters heavily influence pharmacokinetics, defining how effectively a drug candidate traverses lipid bilayer cell membranes. High lipophilicity often increases metabolic clearance and plasma protein binding, whereas low lipophilicity can severely inhibit oral absorption. Because experimental measurement via traditional shake-flask methods can be laborious and resource-intensive, computational prediction tools serve as vital assets for high-throughput virtual screening workflows.