protein conformations (e.g., pairwise atomic distances) and encodes them within a tensor-train structure. The tensor train is a technique to decompose a high-dimensional tensor into lowerdimensional factors, which can more efficiently handle the exponential growth of memory and operations entailed by tensor storage in high-dimensional problems , and has been applied to a broad range of physical processes, including quantum systems . However, it has been observed that the practical application of AMUSEt to biomolecular systems heavily relies on a well-ranked selection of input features ensuring that those representing slow dynamics are prioritized (see Figure and Figure ). This challenge has limited the performance of AMUSEt, making its performance comparable to tICA while falling behind deep-learning methods.