Inspired by these observations and challenges, we first construct a large-scale CPI dataset (CPI2M) with activity cliff annotations and multiple activity types available (K i , K d , EC 50 , and IC 50 ). Then, a crystal structure-free CPI model, called GGAP-CPI (protein Graph and ligand Graph network with Attention Pooling for Compound-Protein Interaction prediction), is proposed for accurate CPI binding affinity and bioactivity prediction. GGAP-CPI adopts a pretrained ligand encoder (KANO ) and a protein embedding generator (ESM-2 ) for advanced representation learning, with a multi-head cross attention pooling assembled to simulate and aggregate the interactions between ligand atoms and protein residues. GGAP-CPI-IntEns (GGAP-CPI trained with an Integrated Ensemble bioactivity learning regime), is proposed to be flexible for general CPI bioactivity prediction.