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Following the strategy outlined during earlier SAMPL challenges we first train 1- and 2-parameter water-free (\u201cdry\u201d) and water-saturated (\u201cwet\u201d) models for <jats:italic>n<\/jats:italic>-octanol solvation Gibbs energies with respect to experimental values from the \u201cMinnesota Solvation Database\u201d (MNSOL), yielding a root mean square error (RMSE) of 1.5\u00a0kcal\u00a0mol<jats:sup>\u22121<\/jats:sup> for the best-performing 2-parameter wet model, while the optimal water model developed for the p<jats:italic>K<\/jats:italic><jats:sub>a<\/jats:sub> part of the SAMPL6 challenge is kept unchanged (RMSE 1.6\u00a0kcal\u00a0mol<jats:sup>\u22121<\/jats:sup> for neutral compounds from a model trained on both neutral and ionic species). Applying these models to the blind prediction set yields a log\u00a0<jats:italic>P<\/jats:italic> 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 <jats:italic>n<\/jats:italic>-octanol are available in the MNSOL database to demonstrate a systematic cause of error and to discuss strategies for improvement.<\/jats:p>","DOI":"10.1007\/s10822-020-00283-4","type":"journal-article","created":{"date-parts":[[2020,1,24]],"date-time":"2020-01-24T07:02:30Z","timestamp":1579849350000},"page":"453-461","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["The SAMPL6 challenge on predicting octanol\u2013water partition coefficients from EC-RISM 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