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PhysChem Suite LogP

Partition Coefficient Calculation

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LogP Overview

Predict Octanol-Water Partition Coefficients from Chemical Structure

ACD/LogP predicts the partition coefficient, a measure of hydrophobicity, from structure.

Use ACD/LogP to:

  • Calculate the partition coefficient (logP) for neutral molecules
  • Identify hydrophilic and hydrophobic fragments of a structure
  • Train the algorithm with experimental measurements

Everything You Need in a LogP Property Calculator

Accurate, Reliable Results

  • Easily evaluate the accuracy of results with a reliability index, a display of similar structures in the database, and literature references for the original experimental data
  • Leverage the knowledge in the extensive training database (>12, 000 compounds)

Deeper Insights

  • Create scatter plots, browse, filter, sort, rank, and prioritize compound libraries with ease

3 LogP Calculators-in-One

  • Results from three algorithms in one software application: Classic, GALAS, and Consensus

Customizable with In-House Data

  • Get the accuracy of an in-house model from a commercial product. Use experimentally measured logP values to expand the applicability domain to proprietary chemical space.
  • Build a training set for each project for fine-tuned accuracy

Calculate the octanol-water distribution coefficient (logP) from chemical structure

See experimental values for similar structures.

Choose from three prediction algorithms. The Classic algorithm provides the calculation protocol

How it Works

Prediction in Seconds with LogP

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  • 1 Draw/import your structure
  • 2 Review results and make decisions
  • 3 Report to PDF or copy/paste
Product Features

Partition Coefficient (LogP) Calculator Features

  • Predict logP from structure (draw in-app, or copy/paste from third-party drawing packages); SMILES string; InChI code; imported MOL, SK2, SKC, or CDX files; or search by name in the built-in dictionary
  • Three prediction algorithms: Classic (default calculator), GALAS (Global,Adjusted Locally According to Similarity), and a Consensus logP based on the other two models.
    Learn about the algorithms
  • Estimates of prediction accuracy in the different models.
    •  Classic
      • Results delivered with 95% confidence intervals for final logP value and incremental contributions
      • All available experimental data and literature references provided for compounds in the internal training library
    •  GALAS
      • Reliability Index
      • Display of 5 most similar structures in the training library with experimental values and literature references
    • Consensus

      • Display of 5 most similar structures in the training library with experimental values and literature references

  • The detailed calculation protocol lists all contributing functional groups, carbon atoms, and interactions through aliphatic, aromatic, and vinylic systems (Classic)
    • Click protocol entry to highlight the corresponding entity on the structure
  • Color highlighting of the molecule to highlight hydrophilic/hydrophobic substructures (GALAS)
  • Calculate logP properties for groups or libraries of compounds and use built-in tools to sort, filter, plot, and rank results.
    • Set user-defined label colors
    • Filter results numerically
    • Sort results by ascending/descending values
  • See results for previously calculated values in the history
  • Report results to PDF
  • Download QPRF and QMRF documents for LogP (GALAS)
  • Train the model with experimental values to improve predictions for proprietary chemical space
    • Create and select different training libraries for calculations, or switch to the built-in algorithm
Deployment/Integration Options

Choose the Deployment Option That Works for You

Desktop/Thick Client

Install ACD/LogP on individual computers to access the thick client, which provides a full graphical user interface and access to algorithm training tools.


Calculate logP for tens of thousands of compounds with minimal user intervention. Batch deployment is compatible with Microsoft Windows and Linux. Plug-in to corporate intranets or workflow tools such as Pipeline Pilot.

Percepta Portal/Thin Client

Use a browser-based application to predict logP. KNIME integration components are available. Host on your corporate intranet or the cloud. Available for Linux and Windows OS.

More Reasons to Use LogP

Technical Information about Partition Coefficient Prediction LogP

A Trainable LogP Calculator

You can use experimentally measured logP values to train the algorithms in ACD/LogP. Improve prediction accuracy and make the model more relevant to your chemical space or project.

Both the Classic and GALAS algorithms can be trained even if you aren’t a computational chemist or software engineer. The Consensus model automatically uses training data applied to the underlying algorithms.

How Are LogP Values Calculated?

Classic Algorithm

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

GALAS Algorithm

The GALAS model is Global, Adjusted Locally According to Similarity. The algorithm is based on a training set of >11,000 compounds and provides a value for logD that is adjusted with data from the most similar compounds.

Consensus Model

The consensus model uses both Classic and GALAS algorithms. Each algorithm is weighted more heavily in regions of chemical space in which it performs most reliably.

When Calculated LogP Is More Accurate Than Experimental

Predicted logP is likely to be more accurate than experimentally measured when:

  • The logP value is outside the reliably measurable range (+8 to -3) obtained by traditional experiments.
  • Tautomeric forms exist. In most cases, logP values cannot be measured for every tautomer.
  • A compound contains acidic and basic groups (e.g., amino acids, peptides, nucleosides). Since these molecules exist in various ionic forms at any given pH, the concentration of the unionized form is negligible.

General Information about LogP

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 hydrophobicity of a compound.

What Is the Difference between LogP and LogD?

Similar to logP (or clogP), logD is also a descriptor of hydrophobicity 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.


What's New!

What’s New in LogP v2022

  • pH Selector tool to help you select the optimal pH for chromatographic separations
  • Capability to import training libraries into PhysChem Suite in all major file types
  • Streamlined “Context Help”