title: All-atom/coarse-grained hybrid predictions of distribution coefficients in SAMPL5.
abstract: We present blind predictions submitted to the SAMPL5 challenge on calculating distribution coefficients. The predictions were based on estimating the solvation free energies in water and cyclohexane of the 53 compounds in the challenge. These free energies were computed using alchemical free energy simulations based on a hybrid all-atom/coarse-grained model. The compounds were treated with the general Amber force field, whereas the solvent molecules were treated with the Elba coarse-grained model. Considering the simplicity of the solvent model and that we approximate the distribution coefficient with the partition coefficient of the neutral species, the predictions are of good accuracy. The correlation coefficient, R is 0.64, 82 % of the predictions have the correct sign and the mean absolute deviation is 1.8 log units. This is on a par with or better than the other simulation-based predictions in the challenge. We present an analysis of the deviations to experiments and compare the predictions to another submission that used all-atom solvent.
authors.affiliation: Department of Chemistry and Molecular Biology, University of Gothenburg, Box 462, SE-405 30, Göteborg, Sweden. samuel.genheden@gu.se.
authors.forename: Samuel
authors.identifier: 0000-0002-7624-7363
authors.initials: S
authors.lastname: Genheden
authors.affiliation: School of Chemistry, University of Southampton, Southampton, SO17 1BJ, UK.
authors.forename: Jonathan W
authors.initials: JW
authors.lastname: Essex
mesh_terms: D003198:Computer Simulation; D003510:Cyclohexanes / Q000737:chemistry*; D008956:Models, Chemical; D015394:Molecular Structure; D004364:Pharmaceutical Preparations / Q000737:chemistry*; D012995:Solubility; D012997:Solvents / Q000737:chemistry*; D013816:Thermodynamics; D014867:Water / Q000737:chemistry*
chemical_list: D003510:Cyclohexanes; D004364:Pharmaceutical Preparations; D012997:Solvents; D014867:Water; C506365:Cyclohexane
keywords: AA/CG; Distribution coefficients; Elba; Hybrid model; Multiscaling; SAMPL5