Currently, we have shown the DeepWEST method to work on simpler systems, i.e., alanine dipeptide in explicit solvation and chignolin in implicit solvation. We have also shown a greater free energy landscape coverage for the NTL9 protein for the DeepWEST method against the conventional WE method. However, extending this method to more complex biological systems comes with challenges. Deep neural networks learn feature transformations and cluster trajectories based on input CVs. The network architecture for such systems primarily depends on the choice of time lag and the number of metastable states we want to cluster. Identifying the slowest processes in complex systems comes with the choice of accurate CVs, which in most cases is unknown. However, recent developments in enhanced MD sampling and MSM approaches would be worth considering. Implementing multi-ensemble Markov models (MEMMs) that conduct large ensembles of even shorter simulations and further accelerate trajectories by adding boosted potentials through various accelerated enhanced sampling methods in DeepWEST could aid the clustering of complex proteins. Implementation of hydrogen-mass repartitioning (HMR) for solvated systems to accelerate the dynamics would prove indispensable in faster learning of metastable states.