For example, 1D representations, e.g. SMILES, or SELFIES 104 are extensively used in cheminformatics when 3D structure isn't essential. Other descriptors, like bond order, the number of aromatic rings, the existence of functional groups are also widely used to decode structural information. More importantly, by means of these chemically intuitive characteristics, it is possible to understand a derived QSAR model. The mapping of descriptors in chemical space provides valuable insights into structural features for targeted properties. By leveraging derived structural information, inverse design of novel molecules is achievable. In terms of NNPs, descriptors play a pivotal role in extracting useful structure features related to energies. Such highly condensed descriptors provide a refined understanding of geometric structures to ML models, particularly those classical models whose simple architectures limit them in terms of interpreting complex 3D structures. As a result, ML models only need to focus on these essential descriptors rather than crude structures, which certainly improves the capability of ML models.