ACD/LogD

Predict the distribution coefficient from structure and understand the lipophilicity of ionizable molecules

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 ACD/LogD prediction module provides the following possibilities:

• Estimate the value of the octanol-water distribution constant, as the logarithmic ratio (logD), from structure at any pH (0–14)
• Adapt the model to proprietary chemical spaces by training the underlying logP or pK_a prediction algorithms

About the Model

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—logP^species (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 pK_a prediction algorithm.

ACD/Percepta offers three different mechanisms of logP prediction (Classic, GALAS, and Consensus) as well as two different approaches to estimate pK_a values (Classic and GALAS) and any of these can be selected as default algorithms for corresponding logP and pK_a 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.

Learn more about the logP and pK_a prediction algorithms.

Training with Experimental Data

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 pK_a predictive models.

Real-world Applications

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.

ACD/Labs Product Suites

The Percepta prediction modules are available as bundles to offer cost savings for multiple modules, and provide related modules as a package.

Other Products

ACD/LogD Model predictions are available as a browser-based thin client application on ACD/Percepta Portal; a thick client product (ACD/Percepta for the Desktop); and a batch calculator (ACD/Percepta Batch).