Generally, multiple starting structures are provided at the beginning of a simulation, and they often fail to establish extensive communication with each other, thereby leading to an initial bias in the simulation. Markov State Models (MSMs) have shown to be successful in generating equilibrium distribution and thereby removing the initial bias in the simulations. The history-augmented Markov state model (haMSM) is another efficient method for the removal of bias in stationary distributions, and it has been implemented recently in WE simulations, where a steady state analysis is performed, and the weights are distributed accordingly to restart the simulation. In short, DeepWEST is an attempt to construct MSMs from short MD simulations, providing a well-sampled initial distribution for the WE simulations. We propose a hybrid method that uses the variational approach for Markov principle (VAMP) and neural networks to identify metastable states from unbiased and short MD simulations as a precursor for running WE simulations. Selected conformations from these states then serve as starting structures for WE simulations to further sample the kinetics and thermodynamics of the system. This hybrid methodology further reduces the computational cost as compared to our previously developed hybrid GaMD-WE method, accelerates the WE approach further by providing well-sampled initial configurations, and provides a better comprehensive picture of the thermodynamic and kinetic properties of the systems of interest by introducing no statistical bias to the free energy landscape to the system. In the forthcoming sections, we will describe the WE method, the VAMP approach, and the hybrid method in detail. We will also demonstrate the capability of the DeepWEST approach to sample kinetic and thermodynamic properties faster than the WE method alone and compare the findings with the already established GaMD-WE approach.