  Molecular Dynamics (MD) simulations are fundamental computational tools for
the study of proteins and their free energy landscapes. However, sampling
protein conformational changes through MD simulations is challenging due to the
relatively long timescales of these processes. Many enhanced sampling
approaches have emerged to tackle this problem, including biased and
path-sampling methods. In this perspective, we focus on adaptive sampling
algorithms. These techniques differ from other approaches because the
thermodynamic ensemble is preserved and the sampling is enhanced solely by
restarting MD trajectories at particularly chosen seeds, rather than
introducing biasing forces. We begin our treatment with an overview of
theoretically transparent methods where we discuss principles and guidelines
for adaptive sampling. Then, we present a brief summary of select methods that
have been applied to realistic systems in the past. Finally, we discuss recent
advances in adaptive sampling methodology powered by machine learning
techniques as well as their shortcomings.
