Loschen, Christoph and Reinisch, Jens and Klamt, Andreas (2020) COSMO-RS based predictions for the SAMPL6 logP challenge. JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN, 34 (4). pp. 385-392. ISSN 0920-654X, 1573-4951
Full text not available from this repository. (Request a copy)Abstract
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 sigma-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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | WATER DISTRIBUTION COEFFICIENTS; PARTITION-COEFFICIENTS; APPROXIMATION; REFINEMENT; ENERGY; COSMO-RS; logP; Octanol-water partition coefficients; Liquid phase thermodynamics; COSMOtherm; COSMOquick; Machine learning |
| Subjects: | 500 Science > 540 Chemistry & allied sciences |
| Divisions: | Chemistry and Pharmacy > Institut für Physikalische und Theoretische Chemie |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 29 Mar 2021 05:21 |
| Last Modified: | 29 Mar 2021 05:21 |
| URI: | https://pred.uni-regensburg.de/id/eprint/44806 |
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