RA-PLA: Retrieval-augmented graph convolutional networks for protein-ligand binding affinity prediction

Protein-ligand binding affinity (PLA) prediction is essential for computational drug discovery, yet existing deep learning methods typically employ a single globally trained model that fails to adapt to individual query samples. In this paper, we propose a novel end-to-end framework that retrieves hard protein-ligand pairs-samples whose nearest neighbors have conflicting labels under manifold smoothness constraints-and integrates them via a semi-supervised graph convolutional network (GCN). For each query, we automatically construct a graph where nodes represent protein-ligand pairs and edges encode pairwise similarities. Our model jointly learns joint descriptors, graph topology, and the GCN predictor. During inference, we fine-tune the model per query using its retrieved hard neighbors.We evaluate on four benchmarks: PDBbind, Davis, KIBA, and BindingDB. Our method consistently outperforms state-of-the-art approaches. On Davis, we achieve a Concordance Index (CI) of 0.952 ± 0.005 (1.6% improvement over NerLTR-DTA) and AUPR of 0.815 ± 0.005 (7.6% improvement). On KIBA, we achieve CI of 0.916 ± 0.002 and AUPR of 0.871 ± 0.005, outperforming DeepCDA by 2.7% and 5.9%, respectively. On PDBbind, our CI of 0.894 ± 0.002 represents an 11.4% improvement over PLA-MoRe. On BindingDB, we achieve AUPR of 0.518 ± 0.009 (5.9% improvement over DeepCDA). In cold-target generalization across four unseen protein families, we achieve average AUPR improvements of 15.5%. Paired t-tests confirm statistical significance (p < 0.05), and ablation studies validate that both hard sample retrieval and end-to-end learning are essential for performance gains. Our framework demonstrates that query-specific adaptation significantly enhances both accuracy and generalization in PLA prediction.