For example, rMSA AF2 can rapidly generate a large number of conformations but does not provide information about how important or representative they are. One of the most common applications of alternative structure generation methods is in downstream drug discovery research, such as molecular docking. These tasks, especially ensemble docking, require generated alternative structures to be classified into meaningful states, such as active/inactive states or open/closed states. Structures within each state live in distinct regions of the high-dimensional protein conformation space. All of these methods fall short of telling us the topologies in this conformation space. Meanwhile, long force-field-based molecular dynamics (MD) sampling remains the gold standard for evaluating or sampling structures even in the age of machine learning. It is frequently used both in the training of deep neural networks and for structural relaxation following model inference. However, it is also well known that brute force MD simulations take an astronomical amount of time to sample protein conformations "sufficiently." Introduced originally in 2023 to sample side chain rotamers, the AlphaFold2-RAVE (af2rave) method combines a hypothesis generator with physics-based sampling methods to address the lack of physical information in structural generation models (Figure ). This pipeline is built in the idea that initiating many short MD simulations from diverse initial structures should be better than waiting for one long MD simulation to cross barriers spontaneously. Thereby, it embeds AlphaFold2-generated diverse structures in a physically meaningful space, providing knowledge about which states are the more important ones and their relative relationships. AlphaFold2-RAVE starts with generating a few carefully selected rMSA AlphaFold2-generated structures that should cover the more than one important conformational states. Then, short MD simulations are launched to sample the local conformational space around these centers and possibly find overlaps across the simulations. Combined, this information can provide an essential understanding of the local landscape spanned by the generated structures. The timeseries data from MD simulations are then analyzed using a machine learning model, State Predictive Information Bottleneck (SPIB), to uncover the underlying topology of these structures and to assign state designations to them. The RAVE protocol (Reweighted autoencoded variational Bayes for enhanced sampling) can be further used to perform enhanced sampling to explore even more in the underrepresented regions of the conformational space. Initially, AlphaFold2-RAVE was demonstrated to identify different states in proteins, ranging from sidechain rotamers to loop motions and for enhanced sampling purposes. Subsequent work extended its application to identifying holo-like structures that can be docked against and are otherwise unattainable from AF2-predicted apo structures. The full af2rave pipeline will be described in detail in the Methods section.