Fan, Shujie published the artcilePrediction of octanol-water partition coefficients for the SAMPL6- log P molecules using molecular dynamics simulations with OPLS-AA, AMBER and CHARMM force fields, Recommanded Product: n-Octanol, the main research area is octanol water partition mol dynamic; AMBER force field; CHARMM force field; Free energy perturbation; Ligand parametrization; Molecular dynamics; OPLS-AA force field; Octanol-water partition coefficient; Solvation free energy.
All-atom mol. dynamics simulations with stratified alchem. free energy calculations were used to predict the octanol-water partition coefficient log Pow of eleven small mols. as part of the SAMPL6- log P blind prediction challenge using four different force field parametrizations: standard OPLS-AA with transferable charges, OPLS-AA with non-transferable CM1A charges, AMBER/GAFF, and CHARMM/CGenFF. Octanol parameters for OPLS-AA, GAFF and CHARMM were validated by comparing the d. as a function of temperature, the chem. potential, and the hydration free energy to exptl. values. The partition coefficients were calculated from the solvation free energy for the compounds in water and pure (“”dry””) octanol or “”wet”” octanol with 27 mol% water dissolved. Absolute solvation free energies were computed by thermodn. integration (TI) and the multistate Bennett acceptance ratio with uncorrelated samples from data generated by an established protocol using 5-ns windowed alchem. free energy perturbation (FEP) calculations with the Gromacs mol. dynamics package. Equilibration of sets of FEP simulations was quantified by a new measure of convergence based on the anal. of forward and time-reversed trajectories. The accuracy of the log Pow predictions was assessed by descriptive statistical measures such as the root mean square error (RMSE) of the data set compared to the exptl. values. Discarding the first 1 ns of each 5-ns window as an equilibration phase had a large effect on the GAFF data, where it improved the RMSE by up to 0.8 log units, while the effect for other data sets was smaller or marginally worsened the agreement. Overall, CGenFF gave the best prediction with RMSE 1.2 log units, although for only eight mols. because the current CGenFF workflow for Gromacs does not generate files for certain halogen-containing compounds Over all eleven compounds, GAFF gave an RMSE of 1.5. The effect of using a mixed water/octanol solvent slightly decreased the accuracy for CGenFF and GAFF and slightly increased it for OPLS-AA. The GAFF and OPLS-AA results displayed a systematic error where mols. were too hydrophobic whereas CGenFF appeared to be more balanced, at least on this small data set.
Journal of Computer-Aided Molecular Design published new progress about Density. 111-87-5 belongs to class alcohols-buliding-blocks, name is n-Octanol, and the molecular formula is C8H18O, Recommanded Product: n-Octanol.
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