The distribution coefficient, D, is a pH dependant measure of the propensity of a molecule to differentially dissolve in two immiscible phases, taking into account all ionized and unionized forms (microspecies). It serves as a quantitative descriptor of lipophilicity. The LogD prediction module within ACD/Percepta provides the following possibilities:
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. Each of those forms has its own tendency to partition between water and n-octanol which is characterised 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. With this information it is possible to calculate logD as a function of the distribution of all molecular species. The latter is governed by pH as predicted by the pKa prediction algorithm.
ACD/Percepta offers three different mechanisms of logP prediction (Classic, GALAS, and Consensus) as well as two different approaches to estimate pKa values (Classic and GALAS) and any of these can be selected as default algorithms for corresponding logP and pKa calculations used to derive logD values. These multiple options, each with its own strengths and limitations, maximize flexibility and allow the user to choose the set-up most suitable to his specific needs.
To improve prediction accuracy and make the model relevant to in-house chemical space or a particular project, the logD prediction module offers the ability for training with experimental data. Because of the nature of the algorithm, the trainability of the logD algorithm is indirect and relies on training the individual logP and pKa predictive models. Despite this fact all trainability features are arranged in a user-friendly manner that makes switching the training on/off and varying certain training sets used for different predictions a trivial task, putting full control in your hands.
Pharmaceuticals—used in medicinal chemistry to assess drug likeness; in pharmacokinetics to help determine ADME profiles (the ability of a drug to be absorbed, successfully reach the intended target, be metabolized and excreted); and in pharmacodynamics to understand target receptor binding.
Agrochemicals—applied similarly to pharmaceuticals with the intention of developing herbicides and insecticides.
Environmental—to model migration of dissolved hydrophobic organics in soil and groundwater to help assess waterway pollution, and toxicity to animals and aquatic life.
ConsumerProducts—used in the formulation of cosmetics, dyes, household cleaners and many other products.
The Percepta prediction modules are available as bundles to offer cost savings for multiple modules, and provide related modules as a package.