{
  "abstract": "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.",
  "abstract_shown": true,
  "affiliations": [
    "Mosaheb Institute for Mathematical Research, Kharazmi University, Tehran, Iran",
    "Computer Engineering and Information Technology Department, University of Qom, Qom, Iran",
    "Department of Artificial Intelligence and Data Science, College of Science and Technology, University of Human Development, Sulaymaniyah, Kurdistan Region ofIraq"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Karim Abbasi"
    },
    {
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        1
      ],
      "name": "Hossein Banadkuki"
    },
    {
      "affs": [
        2
      ],
      "name": "Reza Sepahvand"
    },
    {
      "name": "Shima Rashidi"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9s2wmbamxvrzk.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0357278",
  "fulltext_url": null,
  "id": "9s2wmbamxvrzk",
  "issns": [
    "1932-6203"
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  "landing_url": "https://doi.org/10.1371/journal.pone.0357278",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1703|Computational Drug Discovery Methods"
    ],
    [
      "plos",
      "/Biology and life sciences/Biochemistry/Enzymology/Enzymes/Protein kinases/Tyrosine kinases"
    ],
    [
      "plos",
      "/Biology and life sciences/Biochemistry/Proteins/Enzymes/Protein kinases/Tyrosine kinases"
    ],
    [
      "plos",
      "/Biology and life sciences/Biochemistry/Proteins/Protein interactions"
    ],
    [
      "plos",
      "/Biology and life sciences/Biochemistry/Proteomics/Protein interaction networks"
    ],
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      "/Biology and life sciences/Neuroscience/Cognitive science/Cognitive psychology/Attention"
    ],
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      "/Biology and life sciences/Psychology/Cognitive psychology/Attention"
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    [
      "plos",
      "/Computer and information sciences/Artificial intelligence/Machine learning"
    ],
    [
      "plos",
      "/Computer and information sciences/Network analysis/Protein interaction networks"
    ],
    [
      "plos",
      "/Medicine and health sciences/Pharmacology/Drug interactions"
    ],
    [
      "plos",
      "/Physical sciences/Chemistry/Computational chemistry/Molecular docking"
    ],
    [
      "plos",
      "/Research and analysis methods/Extraction techniques/Protein extraction"
    ],
    [
      "plos",
      "/Social sciences/Psychology/Cognitive psychology/Attention"
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  "openalex": "W7214007265",
  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0357278&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-22",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/9s2wmbamxvrzk.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:43Z",
      "native_id": "10.1371/journal.pone.0357278",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0357278"
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  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "plos:enzymology",
      "subfield": "enzymology-metabolism"
    },
    {
      "field": "biochemistry",
      "origin": "plos:proteomics",
      "subfield": "proteomics-metabolomics"
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    {
      "field": "biology",
      "origin": "plos:neuroscience",
      "subfield": "neuroscience"
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  ],
  "title": "RA-PLA: Retrieval-augmented graph convolutional networks for protein-ligand binding affinity prediction",
  "title_text": "RA-PLA: Retrieval-augmented graph convolutional networks for protein-ligand binding affinity prediction",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:43Z",
  "url": "https://research.draconitic.com/paper/9s2wmbamxvrzk",
  "venue": "PLOS One",
  "versions": []
}