{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T18:04:57Z","timestamp":1790100297110,"version":"4.0.1"},"reference-count":65,"publisher":"Public Library of Science (PLoS)","issue":"9","license":[{"start":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T00:00:00Z","timestamp":1790035200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>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\u2014samples whose nearest neighbors have conflicting labels under manifold smoothness constraints\u2014and 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\u2009\u00b1\u20090.005 (1.6% improvement over NerLTR-DTA) and AUPR of 0.815\u2009\u00b1\u20090.005 (7.6% improvement). On KIBA, we achieve CI of 0.916\u2009\u00b1\u20090.002 and AUPR of 0.871\u2009\u00b1\u20090.005, outperforming DeepCDA by 2.7% and 5.9%, respectively. On PDBbind, our CI of 0.894\u2009\u00b1\u20090.002 represents an 11.4% improvement over PLA-MoRe. On BindingDB, we achieve AUPR of 0.518\u2009\u00b1\u20090.009 (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\u2009&lt;\u20090.05), 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.<\/jats:p>","DOI":"10.1371\/journal.pone.0357278","type":"journal-article","created":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T17:28:52Z","timestamp":1790098132000},"page":"e0357278","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["RA-PLA: Retrieval-augmented graph convolutional networks for protein-ligand binding affinity prediction"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2135-8864","authenticated-orcid":true,"given":"Karim","family":"Abbasi","sequence":"first","affiliation":[{"name":"Mosaheb Institute for Mathematical Research, Kharazmi University","place":["Tehran, 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