Most natural LBSs are located on the surface of a protein because of the high affinity obtained by large interfaces. By utilising the coordinates of LBSs, a molecular docking programme can be employed to identify the potential positions of ligands on the coordinates. (ii) Also, theoretical approaches have been developed to identify potential binding regions. For example, Deep-learning cavity finders are the most effective method ( ), but they suffer from interoperability and extended training time. Recently, the quantum algorithm increased the predictive power of machine learning in a short time ( ). The research provided Polar+, the first biological modelling, and it was tested on quantum computers ( ). However, it has significantly higher training costs than classical machine learning approaches ( ).