Chignolin (PDB ID: 1UAO), a designed protein, is a model system consisting of 10 amino acid residues (GYDPETGTWG) (Figure ). It forms a stable β-hairpin structure in implicit solvent, and MD simulations reveal the slow unfolding of chignolin. Initial coordinates for chignolin were obtained from . The system was then subjected to Generalized Born (GB) implicit solvation using the model II radii. AMBER ff14SB force field was used to prepare the system for MD simulations. Chignolin was subjected to 100 ns of constant volume unbiased MD simulation with a collision frequency of 1 ps -1 , employing a Langevin thermostat at a constant temperature of 300 K. To interpret the kinetics of a system that demonstrates folding and unfolding behavior, we encounter the problem of trajectory alignment with respect to a unique reference structure. Moreover, a large amount of noise would be introduced while the networks transform the data via rotations and translations. Hence for the chignolin system, internal coordinates were chosen as a network input. Nearest-neighbor heavy-atom distances between all nonredundant residues separated by two or more residues served as the network input resulting in 28 nodes. MD trajectories were trained using VAMP neural networks with a time lag of 40 ps, training ratio of 0.9, and a batch size of 1000 that learned discretization in three metastable states by performing a hierarchical decomposition of the state space. A list of hyperparameters used for network training is provided in Table . Starting structures were selected from the metastable states as per the DeepWEST protocol, followed by three independent WE simulations of 40 µs each with a total simulation time of 120 µs. Resampling time, τ was kept to be identical as used in WE and GaMD-WE runs, i.e., 20 ps. CVs for WE simulations were set to be mass-weighted root-mean-square deviation (RMSD) and mass-weighted radius of gyration (R g ) respectively. Both CVs were evenly spaced in bins of 0.02 nm ranging between [0 nm, 1 nm]. The target number of walkers per bin, n w was set to 4 for the WE simulation. To compare the performance of the DeepWEST method with the hybrid GaMD-WE and WE methods, we have identified three metastable states in chignolin as shown in Figure .