Department of Physics, Arizona State University, P.O. Box 871504, Tempe, AZ, 85287-1504, USA.
Université Paris-Saclay, CNRS, Institut de Chimie des Substances Naturelles, UPR 2301, Labex LERMIT, 1 Avenue de la Terrasse, 91198, Gif-sur-Yvette, France.
J Comput Aided Mol Des. 2021 Jul;35(7):853-870. doi: 10.1007/s10822-021-00407-4. Epub 2021 Jul 7.
We predicted water-octanol partition coefficients for the molecules in the SAMPL7 challenge with explicit solvent classical molecular dynamics (MD) simulations. Water hydration free energies and octanol solvation free energies were calculated with a windowed alchemical free energy approach. Three commonly used force fields (AMBER GAFF, CHARMM CGenFF, OPLS-AA) were tested. Special emphasis was placed on converging all simulations, using a criterion developed for the SAMPL6 challenge. In aggregate, over 1000 [Formula: see text]s of simulations were performed, with some free energy windows remaining not fully converged even after 1 [Formula: see text]s of simulation time. Nevertheless, the amount of sampling produced [Formula: see text] estimates with a precision of 0.1 log units or better for converged simulations. Despite being probably as fully sampled as can expected and is feasible, the agreement with experiment remained modest for all force fields, with no force field performing better than 1.6 in root mean squared error. Overall, our results indicate that a large amount of sampling is necessary to produce precise [Formula: see text] predictions for the SAMPL7 compounds and that high precision does not necessarily lead to high accuracy. Thus, fundamental problems remain to be solved for physics-based [Formula: see text] predictions.
我们使用显溶剂经典分子动力学(MD)模拟预测了 SAMPL7 挑战赛中分子的水-辛醇分配系数。通过带窗口的变分自由能方法计算了水合自由能和辛醇溶剂化自由能。测试了三种常用的力场(AMBER GAFF、CHARMM CGenFF、OPLS-AA)。特别强调了使用为 SAMPL6 挑战赛开发的标准来收敛所有模拟。总的来说,进行了超过 1000 个[Formula: see text]的模拟,即使在 1 [Formula: see text]的模拟时间后,某些自由能窗口仍未完全收敛。尽管可能已经进行了尽可能充分的采样,并且是可行的,但对于所有力场,与实验的一致性仍然相当,没有一个力场的均方根误差优于 1.6。总的来说,我们的结果表明,对于 SAMPL7 化合物,需要进行大量采样才能产生精确的[Formula: see text]预测,并且高精度不一定导致高准确性。因此,基于物理的[Formula: see text]预测仍然存在需要解决的基本问题。