In adaptive-biasing methods, a system parameter, which is a function of the atomic coordinates, is introduced in (Figure ). Typically, the evolution of this parameter is associated with a transition between different states of the system, for example, a distance between chains for binding, or a reaction coordinate for a chemical transformation. Because the barriers between the states can be high, sampling all relevant states can be infeasible. Sampling can be improved by adding a biasing potential to the system. In adaptive methods, the bias is applied to the system parameter and dynamically updated until a predefined distribution of the parameter is obtained. Because the parameter is coupled to the coordinates, the biasing potential forces the system to sample the relevant states and the underlying free energy profile can be directly obtained from the bias. In contrast to US simulations, in which sampling is restrained along fixed points on a predefined path that connects the end states, no such path is defined in adaptive-biasing methods. Instead, by applying the bias, a single trajectory samples the full range of the system parameter along multiple pathways. To further speed up sampling, multiple trajectories can be coupled to the same system parameter and run in parallel (multi-walker simulations) as shown in Figure . While several adaptive-biasing methods have been developed, such as metadynamics or adaptive-biasing force, and shown to lead to more reproducible results than US, we opted for using AWH, as this method is available in GROMACS. In AWH, the coupling between the system parameter, called a pseudoparticle, and the reaction coordinate is achieved by a strong harmonic restraint: