ACD/LogP
Predict octanol-water partitioning coefficients from structure
The partition constant, P, is a measure of the propensity of a neutral molecule to differentially dissolve in two immiscible phases, and serves as a quantitative
descriptor of lipophilicity. The logP prediction module provides an estimate of the value of the octanol-water partitioning coefficient (also referred to as KOW)
as the logarithmic ratio (logP), from structure.
LogP predictions are exploited in many of our PhysChem and ADME prediction modules including logD, Oral Bioavailability, Blood-Brain Barrier Permeation,
Passive Absorption, and several Toxicity modules, such as hERG inhibition and Aquatic toxicity.
Available Algorithms
The logP prediction module offers two different predictive algorithms within ACD/Percepta software—Classic and GALAS (Global, Adjusted Locally According to Similarity).
A Consensus logP based on these two models is also available. Experts can investigate each model manually to decide which is more appropriate for particular
chemical space, and provide colleagues with guidelines for use.
Classic
- The primary algorithm calculates logP using the principle of isolating carbons. Well-characterized logP contributions have been compiled for atoms,
structural fragments, and intramolecular interactions derived from >12,000 experimental logP values. A secondary algorithm is applied when unknown fragments are
presented. A detailed description of the original algorithm may be found at "Petrauskas, A., Kolovanov, E., ACD/Log P Method Description. Persp. in Drug Design,
19:1–19, 2000".
- Source of experimental data—peer-reviewed scientific journals and the BioByte Star list.
- Provides a detailed calculation protocol with references for known fragments, and indication of approximated contributions, with mapping onto the structure for easy
interpretation.
GALAS
- Training set: 11,387 compounds; Internal validation: 4890 compounds
- Source of experimental data—reference books (the Merck index, Therapeutic Drugs, Clarke's Isolation and Identification of Drugs), peer-reviewed scientific journals,
and other public data sources such as handbooks and online databases.
- Offers color-coded representation of lipophilic and hydrophilic parts of the compound structure.
- Provides a quantitative estimate of reliability of prediction through the Reliability Index (RI). This number, between 0 and 1, allows you to judge the relevance of
the internal training set to the chemical space being investigated by looking for similar structures and evaluating how well the model performs in the local chemical
environment of the compound (0=poor reliability, either nothing similar exists in the training set, or the model produces inconsistent predictions for similar compounds;
1=excellent reliability, several identical entries present, and model predictions precisely match given experimental values).
- Shows up to 5 of the most similar structures in the internal training set, to help further gauge relevance of the training set to your chemical space.
Consensus LogP
- Uses both the Classic and GALAS algorithms.
- Assigns dynamic adaptive coefficients to each model according to the corresponding indications of prediction quality. As a result, each model obtains larger weight
in those regions of chemical space where it performs most reliably. This allows maximizing the Applicability Domain of the final model and obtaining maximal overall
accuracy for the predicted result.
- Provides the equation used for calculation with dynamic coefficients of both models.
Training with Experimental Data
To improve prediction accuracy and make the model relevant to in-house chemical space or a particular project, the logP prediction module offers the ability
for you to train the model with experimental data. Training is user-friendly and available for both Classic and GALAS algorithms. Training
results are also reflected in Consensus LogP predictions. Training may be switched on, off, or certain training sets used for different projects, giving you full control.
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.
Consumer Products—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.
