Apart from EHRs and EC, various other different biomedical components, such as molecules or proteins, also present challenges at the intersection of biology and artificial intelligence. For molecules, constructing AI models to accurately represent them is a challenging endeavor due to the inherent complexity and vast combinatorial space of molecular structures. The challenge lies in capturing the intricate spatial arrangements of atoms, the diverse chemical bonds, and the nuanced interactions between atoms and functional groups. Additionally, molecules can exist in multiple conformations, making it essential for AI models to encompass the flexibility and dynamics of these structures. MPNN performs iterative message passing between nodes in a graph, allowing information to propagate through the graph's structure to predict molecular property, SphereNet takes into account the 3D position information of the node, EGNN maintains molecular invariance or equivariance under certain transformations such as translation or rotation. In future work, quantum mechanical effects also need to be considered for accurate representation, but it can be computationally intensive and may require specialized techniques.