Our recent work demonstrated the effectiveness of a hybrid enhanced sampling method, the GaMD-WE method, which combines GaMD and WE for an extensive sampling of both the thermodynamics and kinetics of biological systems of interest. GaMD is performed initially to sample the PES of the system by applying several boost potentials and is followed by reweighing to recover the original PES. Configurations selected from the recovered PES are the starting structures for the WE simulations. Several independent boost potentials are applied in parallel GaMD simulations and are reweighted accordingly to recover the PES of the system. The GaMD run with the most PES coverage provides the starting structures for WE simulations. We aim to further decrease the computational cost of thermodynamic and kinetic sampling of systems of interest by running unbiased MD simulations and implementing deep learning with the Markovian variational approach for faster and enhanced hybrid sampling. This way, we can bypass the entire reweighting process to recover the original PES.