Genheden Samuel, Essex Jonathan W
Department of Chemistry and Molecular Biology, University of Gothenburg, Box 462, SE-405 30, Göteborg, Sweden.
School of Chemistry, University of Southampton, Southampton, SO17 1BJ, UK.
J Comput Aided Mol Des. 2016 Nov;30(11):969-976. doi: 10.1007/s10822-016-9926-z. Epub 2016 Jul 26.
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.
我们展示了提交至SAMPL5计算分配系数挑战赛的盲测预测结果。这些预测基于估算挑战赛中53种化合物在水和环己烷中的溶剂化自由能。这些自由能是使用基于混合全原子/粗粒度模型的炼金术自由能模拟计算得出的。化合物采用通用的Amber力场处理,而溶剂分子则采用Elba粗粒度模型处理。考虑到溶剂模型的简单性以及我们用中性物种的分配系数近似分配系数,这些预测具有良好的准确性。相关系数R为0.64,82%的预测具有正确的符号,平均绝对偏差为1.8个对数单位。这与挑战赛中其他基于模拟的预测相当或更好。我们对与实验的偏差进行了分析,并将这些预测与另一份使用全原子溶剂的提交结果进行了比较。