Ligand encoder. Pretrained models were proven to show highly competitive performances on molecular property prediction tasks. In our study, a pretrained molecule encoder (knowledge graph-enhanced molecular contrastive learning with functional prompt, KANO ) was introduced as the ligand encoder to learn advanced atom-level and moleculelevel hidden embeddings. In our study, the KANO without function prompt generator was adopted based on empirical analysis shown in the Supplementary Materials. Concretely, KANO is a L-layered C-MPNN-based graph neural network which is pretrained based on chemical element knowledge graph and contrastive learning. Regarding the L-layered C-MPNN process, given an atom node v and its node set N v for a molecular graph G, an intermediate message vector M (l) (v) in l -th layer is obtained by