The free energy of a process is the fundamental quantity that determines its spontaneity or propensity at a given temperature. In particular, the binding free energy of a drug candidate to its biomolecular target is used as an objective quantity in drug design. Recently, binding kinetics—rates of association (k_on) and dissociation (k_off)—have also demonstrated utility for their ability to predict efficacy and in some cases have been shown to be more predictive than the binding free energy alone. Some methods exist to calculate binding kinetics from molecular simulations, although these are typically more difficult to calculate than the binding affinity as they depend on details of the transition path ensemble. Assessing these rate constants can be difficult, due to uncertainty in the definition of the bound and unbound states, large error bars and the lack of experimental data. As an additional consistency check, rate constants from simulation can be used to calculate free energies (using the log of their ratio) which can then be compared to free energies obtained experimentally or using alchemical free energy perturbation. However, in this calculation it is not straightforward to account for common, practical details such as the finite simulation volume or the particular definition of the “bound” and “unbound” states. Here we derive a set of correction terms that can be applied to calculations of binding free energies using full reactive trajectories. We apply these correction terms to revisit the calculation of binding free energies from rate constants for a host-guest system that was part of a blind prediction challenge, where significant deviations were observed between free energies calculated with rate ratios and those calculated from alchemical perturbation. The correction terms combine to significantly decrease the error with respect to computational benchmarks, from 3.4 to 0.76 kcal/mol. Although these terms were derived with weighted ensemble simulations in mind, some of the correction terms are generally applicable to free energies calculated using physical pathways via methods such as Markov state modeling, metadynamics, milestoning, or umbrella sampling.

过程的自由能是确定给定温度下自发性或倾向性的基本量。特别地，候选药物与其生物分子靶的结合自由能被用作药物设计中的客观量。最近，结合动力学-缔合速率（ķ_在）和解离（ķ_关）-还已经证明了其预测功效的效用，并且在某些情况下比单独的结合自由能更具预测性。存在一些通过分子模拟来计算结合动力学的方法，尽管与结合亲和力相比，这些方法通常比结合亲和力更难计算，因为它们取决于过渡路径整体的细节。由于束缚态和非束缚态定义的不确定性，较大的误差线以及缺乏实验数据，评估这些速率常数可能很困难。作为额外的一致性检查，可以使用来自模拟的速率常数来计算自由能（使用它们的比率的对数），然后将其与通过实验或使用炼金术自由能扰动获得的自由能进行比较。然而，在此计算中，要说明通用的实际细节（例如有限的模拟量或“绑定”和“未绑定”状态的特定定义）并不容易。在这里，我们导出了一组校正项，这些校正项可用于使用完整的反应性轨迹来计算结合自由能。我们应用这些修正项来重新考虑从主客体系统的速率常数计算结合自由能的过程，这是盲目预测挑战的一部分，其中观察到以速率比计算的自由能与通过炼金术扰动计算的自由能之间存在显着偏差。校正项相结合以显着降低相对于计算基准的误差，从3.4降至0.76 kcal / mol。尽管这些术语是在考虑加权集成模拟的情况下得出的，