As demonstrated in the previous sections, the advantage of AMUSET-TICA primarily lies in its highly effective embedding of phase space, achieved by expanding orthogonal tICs into overlapping Gaussian basis functions through a tensor-product data structure. Alternatively, deeplearning-based algorithms, such as VAMPnets and SRVs 56 , can also achieve high accuracy in identifying CVs for biomolecular dynamics by conducting highly nonlinear combinations of input features. When comparing the efficiency of AMUSET-TICA with one such deep-learning method, VAMPnets, for NTL9 and the FIP35 WW domain, VAMPnets demonstrates accuracy comparable to AMUSET-TICA for NTL9 in predicting the three slowest ITS (yellow lines in Figure