In　the　realm　of　drug　discovery　and　design,　the　accurate　prediction　of　protein-ligand　binding　affinity　is　of　paramount　importance　as　it　underpins　the　functional　interactions　within　biological　systems.　This　study　introduces　a　novel　self-supervised　learning　(SSL)　framework　that　combines　contrastive　learning　and　graph　neural　networks　(CL-GNN)　for　predicting　protein-ligand　binding　affinities,　which　is　a　critical　aspect　of　drug　discovery.　Traditional　methods　for　affinity　prediction　are　expensive　and　time-consuming,　prompting　the　development　of　more　efficient　computational　approaches.　CL-GNN　utilizes　a　contrastive　learning　strategy,　a　form　of　SSL,　to　learn　from　a　large　data　set　of　371　458　unique　unlabeled　protein-ligand　complexes.　By　employing　graph　neural　networks　and　molecular　graph　enhancement　techniques,　the　model　effectively　captures　protein-ligand　interactions　in　a　self-supervised　manner.　The　fine-tuned　model　demonstrates　competitive　performance,　achieving　high　Pearson＇s　correlation　coefficients　and　low　root-mean-square　errors　on　benchmark　data　sets.　The　proposed　method　outperforms　existing　machine　learning　models,　showcasing　its　potential　for　accelerating　the　drug　development　process.　The　method　effectively　quantifies　the　similarity　between　protein-ligand　complex　representations　learned　in　the　pretraining　and　downstream　testing　phases　through　cosine　similarity　assessment.　This　approach　not　only　revealed　potential　connections　between　complexes　in　their　binding　properties　but　also　provided　new　insights　into　the　understanding　of　drug　mechanisms　of　action.　In　addition,　the　transparency　of　the　model　is　significantly　improved　by　visualizing　the　importance　of　key　protein　residues　and　ligand　atoms.　This　visualization　tool　provides　insight　into　the　model＇s　predictive　decision-making　process,　providing　key　biological　insights　for　drug　design　and　optimization.