Quality over quantity: Sampling high probability rare events with the Weighted Ensemble algorithm

The prediction of (un)binding rates and free energies is of great significance to the drug design process. Many enhanced sampling algorithms have been developed to predict both relative and absolute binding free energies. All methods come with various sources of error, such as representations of the system (force fields, choice of water model), sampling methods and physical approximations. One means of comparing these tools is through participation in blind prediction challenges such as the Statistical Assessment of Modeling of Proteins and Ligands (SAMPL) challenges. Previously we have shown that free energies and transition rates can be calculated by directly simulating the binding and unbinding processes with our variant of the weighted ensemble algorithm ``Resampling of Ensembles by Variation Optimization'', or ``REVO''. Here, we calculate binding free energies retrospectively for three SAMPL6 host-guest systems and prospectively for a SAMPL9 system to test a modification of REVO that restricts its cloning behavior in quasi-unbound states. Specifically, trajectories cannot clone if they meet a physical requirement that represents a high likelihood of unbinding, which in the case of this work is a center-of-mass to center-of-mass distance. The overall effect of this change was difficult to predict, as it results in fewer unbinding events each of which with a much higher statistical weight. For all four systems tested, this new strategy produced either more accurate unbinding free energies or more consistent results between simulations than the standard REVO algorithm. This approach is highly flexible, and any feature of interest for a system can be used to determine cloning eligibility. These findings thus constitute an important improvement in the calculation of transition rates and binding free energies with the weighted ensemble method.