Fortunately, researchers have developed several "enhanced sampling methods" to overcome this timescale gap between MD simulations and biological processes. Many enhanced sampling methods work by adding a biasing potential to force the system away from metastable states. These include but are not limited to Gaussian accelerated dynamics (GaMD), metadynamics, umbrella sampling, and adaptive biasing force (ABF). Among these, GaMD has the advantage of not requiring any collective variables (CVs) to steer the simulation. Rather, it allows unrestrained sampling of configuration space. Another similar class of methods changes the system's temperature instead to sample states difficult to reach at room temperature, including replica exchange molecular dynamics (REMD) or parallel tempering. Although both of these classes of methods are effective at obtain-ing thermodynamic properties like the free energy landscape of the system, they alter the actual kinetics of the system, preventing them from directly getting kinetic properties from the system. Note that there are methods to derive kinetic properties like rate constants from simulations that used these methods. Still, they need to be obtained either by using Kramers' Rate Theory in the high friction or "overdamping" regime, constructing a master equation, or assuming a low residence time in the transition states. As a result, several path-sampling methods focus on sampling kinetic properties, such as rate constants from the reactant state to the product state, including milestoning, forward flux sampling, transition interface sampling, and others. These methods divide the path space from reactant state to product state into many interfaces and run many short simulations to efficiently obtain rate constants and the free energy landscape of the path space. However, since these methods primarily focus on sampling the path of interest, the rest of the free energy landscape is not extensively sampled. Suppose a more comprehensive picture of the entire configuration space is needed along with its thermodynamic and kinetic properties. In that case, either one can build a Markov state model (MSM) or run the weighted ensemble (WE) method on the system of interest. Both methods decompose the configuration space into small volume elements called "macrostates" and run many short simulations to obtain good statistics. The macrostate construction with feature selection or appropriate CVs and setting parameters such as lag time or simulation time τ differ between the two methods. This difference is due to MSMs requiring Markovian property to hold and being constructed with MD trajectories of the system. In contrast, WE starts sampling the system after the macrostate construction, and parameters such as the simulation time τ are set a priori. Both methods have proven to be useful in uncovering insights into the mechanisms of important biophysical systems. However, both require a sufficient sampling of the configuration space either by having many long MD trajectories or by having many initial configurations to get accurate results quickly.