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Physical & Chemical properties

Partition coefficient

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Administrative data

partition coefficient
Type of information:
Adequacy of study:
weight of evidence
2 (reliable with restrictions)
Rationale for reliability incl. deficiencies:
results derived from a valid (Q)SAR model and falling into its applicability domain, with adequate and reliable documentation / justification
Justification for type of information:

2. MODEL (incl. version number)
ACD/Labs Release 2020.2.0



This module calculates the value of the octanol-water partition coefficient – LogP. LogP predictions are exploited in many of our PhysChem and ADME prediction modules including LogD, Oral Bioavailability, Blood-Brain Barrier Permeation, and Passive Absorption, as well as in several Toxicity modules, such as hERG inhibition or Aquatic toxicity.

Includes two different predictive algorithms – ACD/LogP Classic and ACD/LogP GALAS. A Consensus logP based on these two models is also available.
Provides a quantitative estimate of reliability of prediction by the means of 95% confidence intervals (ACD/LogP Classic), or Reliability Index (ACD/LogP GALAS).
Offers color-coded representation of lipophilic and hydrophilic parts of the compound structure.
Train the model with experimental values to improve predictions for proprietary chemical space

ACD/LogP Classic

Acdlogp classic.png

1. LogP prediction obtained using ACD/LogP Classic calculation algorithm.
2. Press "Configure" button to switch model training on or off, and to select the database file to use for training.
3. LogP calculation protocol. Lists the increments of all functional groups and carbon atoms, as well as the contirbutions of interaction through aliphatic, aromatic and vinylic systems.
4. The protocol is interactive. Click on any entry to highlight the respective atom, group, or interaction onto the molecule.
5. If the compound is found in LogP DB, all available experimental data for that compound are displayed along with literature references.

Technical information

Introduction to the 1-Octanol/Water Partitioning Coefficient

The octanol-water partition coefficient, logPo/w, is a measure of a compound’s hydrophobicity, which in many cases correlates well with various other properties of that compound, such as:
## Extraction coefficients;
## Retention on the reversed phase (RP) layers;
## Transport and permeation through membranes;
## Interaction with biological receptors and enzymes;
## Toxicity;
## Biological potency.

Once you have obtained reliable logP values for a series of compounds, you are able to estimate many of their properties that correlate with logP.

Database of Experimental LogP Values

The main sources of experimental data, comprising the ACD/LogP DB were:
## Reference books: ## The Merck Index. An Encyclopedia of Chemicals, Drugs, and Biologicals, O'Neil, M.J., Smith, A., Heckelman, P.E., Budavari, S., Eds. 13th Edition, Merck & Co., Inc., Whitehouse Station, NJ, 2001
## Therapeutic Drugs, Dolery, C., Ed. 2nd Edition, Churchill Livingstone, New York, NY, 1999
## Clarke's Isolation and Identification of Drugs, Moffat, A.C., Jackson, J.V., Moss, M.S., Widdop, B., Eds. 2nd Edition, The Pharmaceutical Press, London, 1986

## Various articles from peer-reviewed scientific journals*
## Other public data sources (online databases, handbooks, etc.)

* - Articles reporting LogP models by other authors were the predominant type among analyzed literature, meaning that each publication contained larger collections of experimental data (usually in the order of tens or hundreds compounds) compiled from corresponding original experimental articles.

In ACD/Percepta, the internal database is directly accessible and searchable under Databases\LogP data source, where each compound is provided with available experimental LogP values and references to the original literature.

Description of ACD/LogP GALAS Algorithm

ACD/LogP GALAS module provides the estimate of the octanol-water partitioning coefficient for neutral species derived on the basis of GALAS (Global, Adjusted Locally According to Similarity) modeling methodology (please refer to [1] for more details).

Each GALAS model consists of two parts:
## Global (baseline) statistical model that reflects general trends in the variation of the property of interest.
## Similarity-based routine that performs local correction of baseline predictions taking into account the differences between baseline and experimental LogP values for the most similar training set compounds.

GALAS methodology also provides the basis for estimating reliability of predictions by the means of calculated Reliability Index (RI) value that takes into account:
## Similarity of tested compound to the training set molecules.
## Consistence of experimental LogP values and baseline model prediction for the most similar similar compounds from the training set.

Reliability Index ranges from 0 to 1 (0 corresponds to a completely unreliable, and 1 - a highly reliable prediction) and serves as an indication whether a submitted compound falls within the Model Applicability Domain. Compounds obtaining predictions RI < 0.3 are considered outside of the Applicability Domain of the model.

In addition, ACD/LogP GALAS algorithm provides a color-coded representation of the predicted property distribution indicating lipophilic and hydrophilic parts of the compound structure.

Internal Validation

Prior to model development, the compounds comprising the ACD/LogP DB were randomly split into a training set used for building the model, and a test set reserved for validation purposes:
## Training set size: 11,387
## Internal validation set size: 4,890
Description of ACD/LogP Classic Algorithm

When a structure is entered for calculation, the program performs the following procedures:
1. Splits the structure into fragments.
2. Searches for identical fragments in the internal databases: 1. The database of Fragmental Increments contains well-characterized increments for over 500 different functional groups. These differ from each other by their chemical structure (for example, amide, carboxy, ester, etc.), attachment to the hydrocarbon skeleton (aliphatic, vinylic, or aromatic), cyclization (cyclic or non-cyclic), and aromaticity (non-aromatic, aromatic, or fused aromatic).
2. The database of Carbon Atom Increments contains well-characterized increments for different types of carbons that are not involved in any functional group. They differ from each other by their state of hybridization (sp, sp2, or sp3), number of attached hydrogens, branching (primary, secondary, tertiary, or quaternary), cyclization (cyclic or non-cyclic), and aromaticity (non-aromatic, aromatic, or fused aromatic).
3. The database of the Intramolecular Interaction Increments contains well-characterized increments for over 2,000 different types of pair-wise group interactions. They differ from each other by the type of the interacting terminal groups (see the differences among functional groups above), and the length and type of the fragmental system between the interacting groups (aliphatic, aromatic, and vinylic).
4. Searches for identical fragments in the data sources specified for system training (for more information, refer to Appendix D). You can regulate the usage of each data field by its status: Included/Excluded in training, and statistical significance as High/Low.

3. If some fragments are not found in either of the above-mentioned databases, their increments (as well as increments of inter-fragmental interactions) are estimated using Secondary Algorithms.

The algorithm estimates the probability of tautomeric and ionic equilibria, the calculation error, and displays the results.

Data source

ACD/Labs Release 2020.2.0
Bibliographic source:
ACD/Percepta ACD/LogP Classic

Materials and methods

Test guideline
other: REACH Guidance on QSARs R.6

Test material

Chemical structure
Reference substance name:
EC Number:
EC Name:
Cas Number:
Molecular formula:
methyl 2-cyanoprop-2-enoate
Specific details on test material used for the study:

Results and discussion

Partition coefficient
Key result
log Pow
Partition coefficient:
25 °C
Remarks on result:
other: QSAR predicted value

Applicant's summary and conclusion

ACD/Labs predicted that Mecrilate has a log Pow = 0.24