{
  "abstract": "Drug response prediction (DRP) requires models that jointly characterize molecular drug structure and heterogeneous cellular states. Although recent deep learning approaches have integrated multi-omics profiles with drug representations, many treat omics views as parallel feature sources or merge them before relational modeling, which can weaken omics-view-specific cell-line relation structures. Here, we propose MulDR, a structured multi-view relational learning framework that preserves omics-view-specific cell-line relation structures before drug-conditioned cell-drug interaction. MulDR constructs separate relation structures for pathway activity, gene expression, mutation, and DNA methylation, learns view-specific relational cell representations, and uses complementary motif-level, geometry-aware molecular graph, and Morgan fingerprint drug representations to condition their fusion. With this design, cell-line similarity remains view-dependent until the interaction stage, rather than being prematurely compressed into a single feature or relation space. Extensive experiments show that MulDR consistently outperforms representative and recent baselines under the same evaluation protocol, reducing root mean square error by 6.07% relative to the strongest baseline. Cold-start evaluations on unseen drugs and cell lines further show that MulDR generalizes to previously unseen entities, while ablation studies show that removing either view-specific relational modeling or drug-conditioned fusion consistently compromises predictive performance. Together, these results suggest that preserving omics-specific cell-line relations before drug-conditioned interaction modeling provides an effective strategy for robust DRP.",
  "abstract_shown": true,
  "affiliations": [
    "School of Computer Science and Technology, Wuhan University of Science and Technology, 2 Huangjiahuxi Road, Hongshan District, Wuhan 430065, Hubei, China.",
    "School of Computer Science and Artificial Intelligence, Wuhan Textile University, 1 Yangguang Avenue, Jiangxia District, Wuhan 430200, Hubei, China."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Zengwei Wang"
    },
    {
      "affs": [
        0
      ],
      "name": "Wanchen Li"
    },
    {
      "affs": [
        0
      ],
      "name": "Junlin Xu"
    },
    {
      "affs": [
        1
      ],
      "name": "Yajie Meng"
    },
    {
      "affs": [
        0
      ],
      "name": "Hao Zhou"
    },
    {
      "affs": [
        0
      ],
      "name": "Shuting Jin",
      "orcid": "0000-0002-8113-9367"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/arnqcyt70jqwp.bib",
  "cited_by": 0,
  "doi": "10.1093/bib/bbag619",
  "fulltext_url": "https://europepmc.org/articles/PMC13744294",
  "id": "arnqcyt70jqwp",
  "issns": [
    "1467-5463",
    "1477-4054"
  ],
  "landing_url": "https://europepmc.org/article/MED/42998281",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "3002|Computational Drug Discovery Methods"
    ]
  ],
  "openalex": "W7225458115",
  "pdf_url": "https://europepmc.org/articles/PMC13744294?pdf=render",
  "pmcid": "PMC13744294",
  "pmid": "42998281",
  "published": "2026-11-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/arnqcyt70jqwp.ris",
  "sources": [
    {
      "first_seen": "2026-11-09T15:20:34Z",
      "native_id": "MED:42998281",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42998281"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:3002|Computational Drug Discovery Methods",
      "subfield": "medicinal"
    }
  ],
  "title": "Structured multi-view relational learning for drug response prediction.",
  "title_text": "Structured multi-view relational learning for drug response prediction.",
  "type": "peer-reviewed",
  "updated_at": "2026-11-10T03:20:49Z",
  "url": "https://research.draconitic.com/paper/arnqcyt70jqwp",
  "venue": "Briefings in bioinformatics",
  "versions": []
}