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

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dc.contributor.authorTielker, Nicolas
dc.contributor.authorTomazic, Daniel
dc.contributor.authorEberlein, Lukas
dc.contributor.authorGüssregen, Stefan
dc.contributor.authorKast, Stefan M.
dc.date.accessioned2021-03-26T07:48:27Z
dc.date.available2021-03-26T07:48:27Z
dc.date.issued2020-01-24
dc.description.abstractResults 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.en
dc.identifier.urihttp://hdl.handle.net/2003/40106
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-21983
dc.language.isoende
dc.relation.ispartofseriesJ Comput Aided Mol Des;34
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectSAMPL6en
dc.subjectSolvation modelen
dc.subjectQuantum chemistryen
dc.subjectIntegral equation theoryen
dc.subjectEC-RISMen
dc.subjectlog Pen
dc.subject.ddc540
dc.subject.rswkSolvatationde
dc.subject.rswkQuantenchemiede
dc.subject.rswkIntegralgleichungde
dc.subject.rswkOctanolede
dc.subject.rswkWasserde
dc.subject.rswkVerteilungskoeffizientde
dc.titleThe SAMPL6 challenge on predicting octanol–water partition coefficients from EC-RISM theoryen
dc.typeTextde
dc.type.publicationtypearticlede
dcterms.accessRightsopen access
eldorado.secondarypublicationtruede
eldorado.secondarypublication.primarycitationTielker, N., Tomazic, D., Eberlein, L. et al. The SAMPL6 challenge on predicting octanol–water partition coefficients from EC-RISM theory. J Comput Aided Mol Des 34, 453–461 (2020).de
eldorado.secondarypublication.primaryidentifierhttps://doi.org/10.1007/s10822-020-00283-4de

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