COSMO-RS based predictions for the SAMPL6 logP challenge

  • Christoph LoschenEmail author
  • Jens Reinisch
  • Andreas Klamt


Within the framework of the 6th physical property blind challenge (SAMPL6) the authors have participated in predicting the octanol–water partition coefficients (logP) for several small drug like molecules. Those logP values where experimentally known by the organizers but only revealed after the submissions of the predictions. Two different sets of predictions were submitted by the authors, both based on the COSMOtherm implementation of COSMO-RS theory. COSMOtherm predictions using the FINE parametrization level (hmz0n) obtained the highest accuracy among all submissions as measured by the root mean squared error. COSMOquick predictions using a fast algorithm to estimate σ-profiles and an a posterio machine learning correction on top of the COSMOtherm results (3vqbi) scored 3rd out of 91 submissions. Both results underline the high quality of COSMO-RS derived molecular free energies in solution.


COSMO-RS logP Octanol–water partition coefficients Liquid phase thermodynamics COSMOtherm COSMOquick Machine learning 



The authors acknowledge the organizers for setting up the SAMPL6 challenge and the SAMPL NIH Grant 1R01GM124270-01A1 for the support of the experimental work carried out in this context.

Compliance with ethical standards

Conflict of interest

The authors declare the following competing financial interest(s): Andreas Klamt, Jens Reinisch and Christoph Loschen are employees of Dassault Systèmes, BIOVIA. Dassault Systèmes commercially distributes software implementations of COSMO-RS (COSMOtherm, COSMOquick) which were used in the present strudy.

Supplementary material (5 mb)
Supplementary material 1—All COSMO files generated by Turbomole representing the conformations used by COSMOtherm for the calculation of the logP values. Additional information about the influence of the σ-profile fragmentation process on prediction quality and the role of conformational effects. (ZIP 5158 kb)
10822_2019_259_MOESM2_ESM.doc (122 kb)
Supplementary material 2 (DOC 122 kb)


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Dassault SystèmesBIOVIALeverkusenGermany
  2. 2.Institute of Physical and Theoretical ChemistryUniversity of RegensburgRegensburgGermany

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