Standard unbiased MD simulations are limited by the timescale and infrequency of significant biological events. Such events are rarely captured in the trajectory data, and it becomes challenging to analyze further and estimate the kinetics of such transitions. The weighted ensemble simulation approach is an enhanced sampling method designed to sample such rare occurrences or transitions such as protein-protein association reactions and conformational changes by replicating and resampling an ensemble of weighted trajectories. The whole configurational space is subdivided into macrostates or "bins" that sequentially lead the system from an initial state to the target state. Many short simulations or "walkers" carrying probabilities or "weights" are run and the system evolves throughout the simulation through "resampling" that ensures an equal number of trajectories in each bin, i.e., by splitting or merging walkers. These walkers explore the configurational space extensively, eventually sampling rare transitions. Appropriate progress coordinates are used to define the bin boundaries. Transitions between these bins are recorded while the system evolves in time to estimate the kinetics and thermodynamics. The WE method has been demonstrated to accurately estimate the kinetics of rare events for multiple biological systems. Resampling is a crucial component of the WE approach that leads to the statistically unbiased future evolution of the system. Walkers are resampled after every iteration via appropriate replication and reassignment of weights. Weighted trajectories are initiated from assigned bins and are propagated for a short interval, τ . These trajectories are either replicated where there are too few trajectories or deleted where there are more than the required number of trajectories per bin. Since there is no statistical bias in the simulation, thermodynamic and kinetic properties can be directly obtained by the evolution of the weights of the walkers in each bin. Simulation results can be periodically checked for convergence by estimating rate constants for various possible transitions between the macrostates.