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The SAMPL6 challenge on predicting octanol-water partition coefficients from EC-RISM theory.

Authors
  • Tielker, Nicolas1
  • Tomazic, Daniel1
  • Eberlein, Lukas1
  • Güssregen, Stefan2
  • Kast, Stefan M3
  • 1 Physikalische Chemie III, Technische Universität Dortmund, Otto-Hahn-Str. 4a, 44227, Dortmund, Germany. , (Germany)
  • 2 Sanofi-Aventis Deutschland GmbH, R&D Integrated Drug Discovery, 65926, Frankfurt am Main, Germany. , (Germany)
  • 3 Physikalische Chemie III, Technische Universität Dortmund, Otto-Hahn-Str. 4a, 44227, Dortmund, Germany. [email protected] , (Germany)
Type
Published Article
Journal
Journal of Computer-Aided Molecular Design
Publisher
Springer-Verlag
Publication Date
Apr 01, 2020
Volume
34
Issue
4
Pages
453–461
Identifiers
DOI: 10.1007/s10822-020-00283-4
PMID: 31981015
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Results are reported for octanol-water partition coefficients (log P) of the neutral states of drug-like molecules provided during the SAMPL6 (Statistical Assessment of Modeling of Proteins and Ligands) blind prediction challenge from applying the "embedded cluster reference interaction site model" (EC-RISM) as a solvation model for quantum-chemical calculations. Following the strategy outlined during earlier SAMPL challenges we first train 1- and 2-parameter water-free ("dry") and water-saturated ("wet") models for n-octanol solvation Gibbs energies with respect to experimental values from the "Minnesota Solvation Database" (MNSOL), yielding a root mean square error (RMSE) of 1.5 kcal mol-1 for the best-performing 2-parameter wet model, while the optimal water model developed for the pKa part of the SAMPL6 challenge is kept unchanged (RMSE 1.6 kcal mol-1 for neutral compounds from a model trained on both neutral and ionic species). Applying these models to the blind prediction set yields a log P RMSE of less than 0.5 for our best model (2-parameters, wet). Further analysis of our results reveals that a single compound is responsible for most of the error, SM15, without which the RMSE drops to 0.2. Since this is the only compound in the challenge dataset with a hydroxyl group we investigate other alcohols for which Gibbs energy of solvation data for both water and n-octanol are available in the MNSOL database to demonstrate a systematic cause of error and to discuss strategies for improvement.

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