Understanding interactions within biomolecules is crucial for many topics such as drug discovery. Some structural modifications, sometime undetected by experiment, can drastically change the nature of the physics ruling interacting complex systems. For this reason, predicting the long timsecale conformational dynamics of proteins is a long standing challenge within the conventional Molecular Dynamics (cMD) community. It requires accurate models able to capture the true potential energy hyper-surface and long simulations to both access the large biological processes time-scale and satisfy the ergodicity principle. Accelerating MD has been therefore a central field of research in the last decades. Beside these developments, several additional strategies have been pursued overs the years to further speed up the simulations. They include the extensive use of High Performance Computing (HPC) ressources and the optimization of GPU-accelerated modeling platforms. Alternatively, an intensive algorithmic work has been undertaken, introducing techniques such as multiple-time-step integrator schemes or collective variables-driven molecular dynamics methods. The latter have been found useful in enhanced sampling and free energy calculation. Although such methods are powerful as they can estimate free energies of binding or the stability of secondary and quaternary structures of proteins, the free energy estimations can suffer from biases either generated by the initial choice of the collective variable (CV) or by the existence of multiple CV within the mechanism process (e.g dual mechanisms). For these reasons, collective variable-free methods have become increasingly popular. Among them, the recent Gaussian accelerated Molecular Dynamics (GaMD) has shown great promises due to its high sampling acceleration, its user-friendly tunable parameters and its minor additional computational cost. GaMD accelerates conformational sampling by adding a harmonic boost to the potential energy. Coupled with the second order cumulant expansion, GaMD allows us to compute unbiased properties by using an accurate reweighting procedure through cumulant expansion to the second order.