A methodology that combines alchemical free energy calculations (FEP) with machine learning (ML) has been developed to compute accurate absolute hydration free energies. The hybrid FEP/ML methodology was trained on a subset of the FreeSolv database and retrospectively shown to outperform most submissions from the SAMPL4 competition. Compared to pure machine-learning approaches, FEP/ML yields more precise estimates of free energies of hydration and requires a fraction of the training set size to outperform standalone FEP calculations. The ML-derived correction terms are further shown to be transferable to a range of related FEP simulation protocols. The approach may be used to inexpensively improve the accuracy of FEP calculations and to flag molecules which will benefit the most from bespoke force field parametrization efforts.

中文翻译：

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水合自由能计算的混合炼金术自由能/机器学习方法。

已经开发了将炼金术自由能计算（FEP）与机器学习（ML）结合起来的方法，以计算准确的绝对水合自由能。FEP / ML混合方法论在FreeSolv数据库的一个子集上进行了训练，并被追溯证明优于SAMPL4竞赛中的大多数论文。与纯机器学习方法相比，FEP / ML可以更精确地估计水合自由能，并且需要训练集大小的一小部分才能胜过独立FEP计算。进一步证明了ML衍生的校正项可以转移到一系列相关的FEP仿真协议中。该方法可用于廉价地提高FEP计算的准确性，并标记将从定制力场参数化工作中受益最多的分子。