For CASF-2016, we collected 19,443 protein-ligand binding complexes along with their experimental binding affinities from the PDBbind-v2020 general set, with 285 of them identified as the CASF-2016 benchmark. The ligand and protein AlphaFold 2-predicted structures and sequences were taken as input for GGAP-CPI-IntEns and other CPI baselines to directly predict binding affinities. The performance results, listed in Table , show that GGAP-CPI-IntEns consistently performed competitively than other CPI-SFs in terms of scoring power and ranking power, with relative improvements of 24.44% for ranking power compared to the next best methods (ECFP-ESM-GBMr). Compared to other SFs, GGAP-CPI-IntEns is comparable to most traditional-SFs, DL-docking, and DL-SFs, such as AutoDock Vina, 10 CarsiDock, and RTMScore. It is important to note that GGAP-CPI-IntEns and all CPI-SFs were trained using complex structure-free CPI data without any binding pose or crystal structure information.