To generate well-sampled initial conformations for WE simulations, we resort to a neural network architecture that employs the variational approach for Markov processes (VAMP), known as VAMPnets, which is a non-biased trajectory learning approach towards faster estimation of kinetics and thermodynamics of systems of interest as compared to other hybrid approaches. MD data analysis is primarily performed in subsequent steps of featurization, dimension reduction, discretization, and coarse-graining. Featurization is the process where the simulation data is often subjected to the removal of translational and rotational motion and/or transformed into internal coordinates. Featurization follows a dimensionality reduction where a high dimensional trajectory data is reduced to slow collective variables. However, initial steps of featurization, such as choosing appropriate metrics for training the trajectory dataset such as using the cartesian coordinates or internal coordinates, selecting suitable CVs that describe the conformation space such as dihedral angles RMSD, the radius of gyration, removal of solvent coordinates, selection of heavy atoms, and realignment are to be determined before training the trajectory using VAMPnets. The resultant metric space is then discretized to fewer states, and the process is called discretization. Finally, a coarse-graining of the Markov state model (MSM) is performed since the internal dynamics of a particular set of microstates can be faster than the modeling timescales. All of the above steps involved in the process of generating MSMs are performed by VAMPNets that learn the non-linear collective variables or reaction coordinates that separate the metastable states of biological systems.