{
  "abstract": "Motivation: Quantifying predictive uncertainty is essential for computational drug discovery to reduce experimental costs and false positives. However, most drug-target affinity (DTA) models focus on improving prediction accuracy without reliable uncertainty estimates.\n\nResults: We propose representation-aware conformal prediction (RCP)-DTA, a representation-aware conformal prediction framework that constructs adaptive prediction intervals based on feature similarity. By constructing local calibration sets, our approach generates sample-specific prediction intervals that achieve empirical coverage close to the nominal confidence level. To improve representation quality, RCP-DTA employs a cross-modal architecture that integrates pretrained sequence representations and structural information. Binding site context is incorporated via virtual pocket feedback to enhance residue representations, followed by gated fusion for adaptive integration of sequence and structural modalities. Evaluations across multiple DTA benchmark datasets demonstrate that RCP-DTA achieves competitive affinity prediction performance while providing reliable uncertainty quantification.\n\nAvailability and implementation: Code and data are available at: https://github.com/Lee515628/RCP-DTA",
  "abstract_shown": true,
  "affiliations": [
    "Department of Mathematics, School of Mathematics and Computer Sciences, Nanchang University , Nanchang 330031,"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Xinya Li"
    },
    {
      "affs": [
        0
      ],
      "name": "Yaxin Zhang"
    },
    {
      "affs": [
        0
      ],
      "name": "Ximin Zeng"
    },
    {
      "affs": [
        0
      ],
      "name": "Danping Zhou"
    },
    {
      "affs": [
        0
      ],
      "name": "Yue Cheng"
    },
    {
      "affs": [
        0
      ],
      "name": "Shaoping Shi",
      "orcid": "0000-0003-0045-868X"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/22z1zd44szd4f.bib",
  "cited_by": 0,
  "doi": "10.1093/bioinformatics/btag771",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13719223/",
  "id": "22z1zd44szd4f",
  "issns": [
    "1367-4803",
    "1367-4811"
  ],
  "landing_url": "https://academic.oup.com/bioinformatics/article/doi/10.1093/bioinformatics/btag771/8914808",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "AcademicSubjects/SCI01060"
    ],
    [
      "heading",
      "Original Paper"
    ],
    [
      "heading",
      "Sequence Analysis"
    ],
    [
      "openalex",
      "1703|Computational Drug Discovery Methods"
    ]
  ],
  "openalex": "W7221832627",
  "pdf_url": "https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btag771/71920955/btag771.pdf",
  "pmcid": "PMC13719223",
  "pmid": "42870399",
  "published": "2026-10-13",
  "publisher": "Oxford University Press (OUP)",
  "ris": "https://research.draconitic.com/paper/22z1zd44szd4f.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:38Z",
      "native_id": "10.1093/bioinformatics/btag771",
      "source": "crossref",
      "url": "https://academic.oup.com/bioinformatics/article/doi/10.1093/bioinformatics/btag771/8914808"
    },
    {
      "first_seen": "2026-11-02T12:23:05Z",
      "native_id": "PMC13719223",
      "source": "pmc",
      "url": "https://doi.org/10.1093/bioinformatics/btag771"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "issn:1367-4803",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Representation-aware conformal prediction for reliable uncertainty quantification in drug-target affinity",
  "title_text": "Representation-aware conformal prediction for reliable uncertainty quantification in drug-target affinity",
  "type": "peer-reviewed",
  "updated_at": "2026-11-02T12:23:05Z",
  "url": "https://research.draconitic.com/paper/22z1zd44szd4f",
  "venue": "Bioinformatics",
  "versions": []
}