The target number of walkers per bin, n w was set to be 4 for the WE simulation. To compare the performance of the DeepWEST method with the WE and the hybrid GAMD-WE method, we have identified three metastable states in alanine dipeptide as shown in Figure (a). Note that Figure (b) shows the three metasbale states when the simulation time was 250 ns. For the DeepWEST method, we have used the 50 ns simulation time as an input trajectory to be trained by the network architecture. The metastable states were chosen in such a way that each state represents an important region of the PES. These regions are α R , α L , and P II . α R lies within the α region while P II lies in the β region of the Two different WE approaches, namely the conventional WE and the hybrid GaMD-WE approach, were compared to assess the performance of our newly developed DeepWEST Table : Rate constants (ns -1 ) between different metastable states of alanine dipeptide obtained after 12 µs of aggregate simulation time. In the case of brute force simulation, the first value indicates the average rate constant while the value within the brackets indicates the 95% confidence interval computed using Bayesian bootstrapping. For WE, GaMD-WE, and the DeepWEST methods, the error bars represent 95% confidence intervals obtained from the standard deviation of three independent runs. : Evolution of rate constants over aggregate simulation time for brute force simulation (black), WE simulations (red), GAMD-WE approach (blue), and DeepWEST approach (green). For WE, GaMD-WE, and the DeepWEST methods, the error bars represent 95% confidence intervals obtained from the standard deviation of three independent runs. approach. WE was run for a total simulation time of 36 µs averaged over three independent runs of 12 µs each. Similarly, the GaMD-WE approach was run for 50 ns of GaMD followed by 11.95 µs of WE, totaling a simulation time of 36 µs averaged over three independent runs of of 12 µs each. Likewise, the DeepWEST approach was run for an unbiased MD simulation of 50 ns followed by 11.95 µs of WE simulation, totaling a simulation time of 36 µs averaged over three independent runs of 12 µs each. Figure demonstrates the ability of the deep-learned kinetic modeling approach to cluster the MSMs and also ensures that adequate sampling has been achieved to select starting conformations. The average free energy is given by -k B T lnP where k B is the Boltzmann constant, T is the temperature, and P is the probability. Figure and Figure represent the average free energy profiles of alanine dipeptide for the WE and the hybrid GaMD-WE approach, respectively, while Figure represents the free energy profile for the DeepWEST approach. Figure shows almost identical PES coverages obtained through the DeepWEST approach as compared to the WE (Figure ) and the hybrid GaMD-WE approach (Figure ). This, in turn, demonstrates the ability of the DeepWEST approach in accessing the entire free energy landscape of the system with enough sampling in the P II , α L , and the right-handed α-helix or α R region. Figure demonstrates the average free energy profiles of alanine dipeptide from three separate runs of WE, GaMD-WE, and DeepWEST simulations at different stages the simulation.