Bulding upon this approach, modern architectures such as SchNet provide a way to quickly extract relevant features to pass through the VAMPnet . GraphVAMPnets architectures extends this approach by incorporating graph neural networks to better capture local structural and temporal dependencies . This enables GraphVAMPnets to be particularly useful for assembly studies such as protein folding and biomolecular oligomerization. GraphVAMPnets leverage graph structures to represent interactions between atoms or residues, allowing the neural network to process spatial relationships more naturally and improve the discovery of slow, functionally relevant transitions. While VAMPnets presents an automated approach to easily generate MSM-like models, there is a significant dependence of model quality on hyperparameters. In fact, recent results highlight that it is not only difficult to automatically select appropriate hyperparameters to maximize VAMP-2 score, but enforcing physical constraints can result in inconsistent model generation . As such, model Chapter generation and variational scores should only provide guidelines for appropriate model construction, and cannot serve as the sole discriminator.