Registration Dossier

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Please be aware that this old REACH registration data factsheet is no longer maintained; it remains frozen as of 19th May 2023.

The new ECHA CHEM database has been released by ECHA, and it now contains all REACH registration data. There are more details on the transition of ECHA's published data to ECHA CHEM here.

Diss Factsheets

Administrative data

adsorption / desorption: screening
Type of information:
Adequacy of study:
other information
2 (reliable with restrictions)
Rationale for reliability incl. deficiencies:
results derived from a valid (Q)SAR model and falling into its applicability domain, with limited documentation / justification

Data source

Reference Type:
other: QSAR model
Bibliographic source:
OPERA-model for organic carbon-sorption coefficient

Materials and methods

Test guideline
according to guideline
Version / remarks:
ECHA guidance on information requirements and chemical safety assessment Chapter R.6: QSARs and grouping of chemicals.
Principles of method if other than guideline:
[1]An automated curation procedure for addressing chemical errors and inconsistencies in public datasets used in QSAR modeling. 2016. Kamel Mansouri, Chris M. Grulke, Ann M. Richard, Richard
S. Judson and Antony J. Williams. SAR & QSAR in Environ. Res; Vol. 27 , Iss. 11,2016. doi: 10.1080/1062936X.2016.1253611.
[2]OPERA: A free and open source QSAR tool for physicochemical properties and environmental fate predictions. Kamel Mansouri, Chris Grulke, Richard Judson, Antony Williams, Journal of Cheminformatics (2017)
[3]PaDEL-descriptor: an open source software to calculate molecular descriptors and fingerprints. Chun Wei Yap. (2011). J. Comput. Chem., 32: 1466–1474. doi:10.1002/jcc.21707
[4]A KNIME workflow for chemical structures curation and standardization in QSAR modeling. Kamel Mansouri, Sherif Farag, Jayaram Kancherla, Regina Politi, Eugene Muratov, Denis Fourches, Nikolai Nikolov, Eva Bay Wedebay, Christopher Grulke, Ann Richard, Richard Judson, Alexander Tropsha. (in preparation)
[5]The influence of data curation on QSAR Modeling – examining issues of quality versus quantity of data (SOT). Williams, A., K. Mansouri, A. Richard, AND C. Grulke. Presented at Society of Toxicology, New Orleans, LA, March 13 - 17, 2016.
[6]An Online Prediction Platform to Support the Environmental Sciences (American Chemical Society). Richard, A., C. Grulke, K. Mansouri, R. Judson, AND A. Williams. Presented at ACS Spring Meeting, San Diego, CA, March 13 - 17, 2016.
[7]The importance of data curation on QSAR Modeling: PHYSPROP open data as a case study. Kamel Mansouri, Christopher Grulke Ann Richard Richard Judson Antony Williams. Presented at QSAR2016 14 June 2016, Miami, FL
[8]Mansouri K. (2017) OPERA: A QSAR tool for physicochemical properties and environmental fate predictions. doi: 10.6084/m9.figshare.4836428 ental_fate_predictions/4836428

Test material

Constituent 1
Chemical structure
Reference substance name:
Dimethyl succinate
EC Number:
EC Name:
Dimethyl succinate
Cas Number:
Molecular formula:
dimethyl succinate
Specific details on test material used for the study:

Results and discussion

Adsorption coefficient
25.7 L/kg
Remarks on result:
other: QSAR prediction

Any other information on results incl. tables

The molecule is included in the applicability domain of the model:

Global applicability domain: Inside
Local applicability domain index: 0.522
Confidence level: 0.531

Applicant's summary and conclusion

Validity criteria fulfilled:
The substance has a predicted Koc of 25.7 L/kg.