Loschen, Christoph ; Reinisch, Jens ; Klamt, Andreas
Alternative Links zum Volltext:DOIVerlag
| Dokumentenart: | Artikel |
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| Titel eines Journals oder einer Zeitschrift: | Journal of Computer-Aided Molecular Design |
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| Verlag: | SPRINGER |
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| Ort der Veröffentlichung: | DORDRECHT |
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| Band: | 34 |
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| Nummer des Zeitschriftenheftes oder des Kapitels: | 4 |
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| Seitenbereich: | S. 385-392 |
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| Datum: | 2020 |
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| Institutionen: | Chemie und Pharmazie > Institut für Physikalische und Theoretische Chemie |
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| Identifikationsnummer: | | Wert | Typ |
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| 10.1007/s10822-019-00259-z | DOI |
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| Stichwörter / Keywords: | WATER DISTRIBUTION COEFFICIENTS; PARTITION-COEFFICIENTS; APPROXIMATION; REFINEMENT; ENERGY; COSMO-RS; logP; Octanol-water partition coefficients; Liquid phase thermodynamics; COSMOtherm; COSMOquick; Machine learning |
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| Dewey-Dezimal-Klassifikation: | 500 Naturwissenschaften und Mathematik > 540 Chemie |
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| Status: | Veröffentlicht |
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| Begutachtet: | Ja, diese Version wurde begutachtet |
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| An der Universität Regensburg entstanden: | Ja |
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| Dokumenten-ID: | 50135 |
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Web of Science
Zusammenfassung
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 ...
Zusammenfassung
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.
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