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Draw a chemical structure or copy/paste from your favorite drawing package for the most accurate distribution coefficient values, across all compound species, at various pHs.
View logD results as a list at discrete pH values, or navigate a plot for logD at a pH of interest.
The distribution coefficient is the ratio of the sum of the concentrations of all forms of the compound (both neutral and ionized) in each of the two phases (octanol and an aqueous buffer). Each of those forms has its own tendency to partition between aqueous buffer and n-octanol. This is characterized by a pH independent partitioning coefficient—logPspecies (e.g., logP0, logP-, logP2+,etc.).
The logD prediction algorithm calculates such partitioning constants based on the fragmental algorithm of logP for the neutral form and a series of correction factors, taking into account aspects such as the type and position of ionization in the molecule. LogD, the distribution coefficient is calculated as a function of the distribution of all molecular species, governed by pH as predicted by the pKa.
Percepta offers various algorithms for prediction of logP and pKa values and any combinations of these can be selected (and set as default) for prediction of the distribution coefficient (logD).
Since logD calculations rely on the logP and pKa prediction algorithms, logD trainability may be achieved through training of the individual logP and pKa models. This helps to improve prediction accuracy and expand the applicability of the model to novel chemical space.
Desktop/Thick client
Software installations for individual computers with a graphical user interface. Full physicochemical, ADME and toxicity calculator modules are available (with training capabilities) including the PhysChem Profiler bundle.
Batch
Screen tens of thousands of compounds with minimal user intervention—compatible with Microsoft Windows and Linux operating systems (OS). Plug-in to corporate intranets or workflow tools such as Pipeline Pilot.
Percepta Portal/Thin client
Web-based application for prediction of molecular properties (PhysChem, ADME, and toxicity) and data analysis. KNIME integration components available.
Host on your corporate intranet or the cloud. Available for Linux and Windows OS.
See how well the ACD/LogD module performed in predicting logD for 1659 pharmaceutical compounds. Also included are the results before and after training the algorithm with experimental data.
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This application note discusses the importance of using the machine learning (model training) capabilities of predictive models to improve accuracy.
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LogP vs logD? Learn why logD is the most relevant lipophilicity descriptor in R&D and see how it may be applied in drug discovery to understand physiological/in vivo behaviors.
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The distribution constant (D), also known as the apparent partition coefficient, is a measure of the hydrophilicity ('aqueous-loving') or hydrophobicity ('aqueous-fearing') an ionizable molecule is. It represents the tendency of a compound to differentially dissolve in two immiscible phases (typically, octanol and aqueous buffer), while taking into the account the distribution of ionized species based on pH. It is also referred to as the distribution coefficient. For example, see the partitioning of methylamine in octanol-water and how the distribution constant describes the partitioning:
LogD prediction models estimate the distribution constant as a logarithmic ratio (logD or ClogD). The distribution coefficient acts as a quantitative descriptor of the lipophilicity (or hydrophobicity) of ionizable compounds.
Drug Discovery—LogD values are used to understand and predict the in-vivo behavior of an active compound under physiological conditions. Since the pH environment is very different throughout the gastrointestinal tract (ranging from 1.4 in the stomach to 8 in the colon) understanding the lipophilicity/hydrophilicity of a drug lead under physiological conditions helps scientists understand behavior under substantially different chemical environs.
LogD values are also important in chromatographic method development (for selection of the appropriate pH and columns for separations; in agrochemistry to develop herbicides and insecticides; in environmental chemistry to understand the behavior of pollutants, and the in development of many other consumer products.
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