Partition Coefficient Calculator | ACD/LogP Software
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Partition Coefficient Calculation with ACD/LogP


  • Calculate the octanol-water distribution coefficient from chemical structure
    Calculate the octanol-water distribution coefficient from chemical structure
  • Use machine learning capabilities of the software to expand its applicability to novel chemical space
    Use machine learning capabilities of the software to expand its applicability to novel chemical space

Predict octanol-water partition coefficients from chemical structure

Draw a chemical structure or copy/paste from your favorite drawing package for the most accurate logP values in the industry.

Calculations may also be made from chemical name or SMILES string.

How are logP values calculated?

ACD/LogP includes a variety of algorithms for prediction of partition coefficient. The combination of algorithms delivers coverage for a broad chemical space and provides the most accurate logP values for your compounds.

Classic Algorithm

Based on >12,000 experimental logP values, the Classic algorithm uses the principal of isolating carbons

GALAS Algorithm

Based on a training set of >11,000 compounds GALAS provides a value for logP that is adjusted with data from the most similar compounds.

Consensus Model

Using both Classic and GALAS algorithms, the consensus algorithm weights the calculation to the model best suited for each structure.

LogP Calculator Features

  • 3 different algorithms (Classic, GALAS, and Consensus) for calculation of lipophilicity
  • Quantitative estimates of prediction reliability—confidence intervals in ACD/LogP Classic, and Reliability Index in ACD/LogP GALAS
  • Color-coded representation of lipophilic/hrdrophilic contributions of structural fragments
  • Expand model applicability and improve accuracy for novel chemical space by training with experimental data

  • The ACD/LogP GALAS model offers structure highlighting and features to help assess prediction accuracy
    The ACD/LogP GALAS model offers structure highlighting and features to help assess prediction accuracy
  • The ACD/LogP Consensus model provides logP values as a weighted average of the GALAS and Classic algorithms
    The ACD/LogP Consensus model provides logP values as a weighted average of the GALAS and Classic algorithms
  • The ACD/LogP Classic model offers structure highlighting and features to help assess prediction accuracy
    The ACD/LogP Classic model offers structure highlighting and features to help assess prediction accuracy

A Machine Learning LogP Calculator

If you experimentally measure logP values, you can use this experimental data to train the algorithms. This improves prediction accuracy and makes the model more relevant to your chemical space or project.

Both the Classic and GALAS algorithms offer machine learning (training) and you don't have to be a computational chemist to use them. The Consensus model automatically uses training data applied to either or both of the underlying algorithms.

Deployment Options



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.

Resources

Blog Article
Partitioning (logP or logD)—are you using/measuring the right descriptor?

At best chemists use the terms logP (clogP) and logD interchangeably, at worst they don't know what logD is or that it is the lipophilicity descriptor they should use.
Read more

AppNote
The Importance of Training Predictors

This application note discusses the importance of using the machine learning (model training) capabilities of predictive models to improve accuracy.
Read more

What is the partition coefficient?

The partition constant (P) is a measure of how hydrophilic ('water-loving') or hydrophobic ('water-fearing') a neutral (uncharged) molecule is. It represents the tendency of a compound to differentially dissolve in these two immiscible phases (typically, Octanol and Water). The partition coefficient is also referred to as Kow and the octanol/water partition coefficient.

LogP prediction models estimate this value as a logarithmic ratio (logP, ClogP, or AlogP). The partition coefficient acts as a quantitative descriptor of the lipophilicity (or hydrophobicity) of a compound.

What is the Difference between LogP and LogD?

Similar to logP (or clogP), logD is also a descriptor of lipophilicity but it is not limited to describing the neutral molecule. LogD is a measure of the hydrophobicity for ionizable compounds which takes into account pH dependence.

How are logP values used in R&D?

Hydrophobicity (as determined by logP) can help explain or predict the behavior of a compound and is useful in many industries:

Pharmaceuticals—logP helps medicinal chemists assess drug likeness; in pharmacokinetics it can help determine the ADME profile—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—Kow values are used to help develop herbicides and insecticides. Partitioning values help determine whether a compound will reach its intended action site and the likelihood of environmental pollution.

Environmental—partition coefficients are used to model the migration of dissolved hydrophobic organics in soil and groundwater to help assess waterway pollution, and toxicity to animals and aquatic life.

Consumer Products—an understanding of partitioning is used in the formulation of cosmetics, dyes, household cleaners, and many other products.

Ready to learn more?

Contact us to request a demonstration of our software, or to answer any questions you may have.

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