{
  "abstract": "Motivation: Designing compounds capable of modulating multiple target proteins simultaneously (i.e. polypharmacology) is critical for treating complex diseases such as cancer and neurodegenerative disorders. However, AI-driven multitarget drug design remains severely hindered by the scarcity of multitarget 3D structural data and the inefficiency of traditional “generate-then-filter” pipelines. There is a pressing need for scalable methods that can satisfy diverse binding constraints across multiple proteins without relying on scarce structural priors.\n\nResults: To overcome these bottlenecks, we reformulate multitarget drug design as a sequence-based generation task and introduce PolyTamGen, an end-to-end autoregressive framework. We first curate a large-scale ligand-centric bioactivity dataset from ChEMBL and BindingDB, comprising 1.6 million unique ligands and 4.9 million bioactivity entries. PolyTamGen encodes target protein sets using ESM3, compresses these high-dimensional features into compact tokens via an attention module, and feeds them into a sequence-based compound generator to produce the final multitarget ligands. We further apply Direct Preference Optimization to improve docking-based affinity proxies and drug-likeness. Extensive experiments on dual- and triple-target benchmarks demonstrate that PolyTamGen improves joint success rates over representative baselines while maintaining high chemical validity and diversity.\n\nAvailability and implementation: The code is available at https://github.com/bjzgcai/PolyTamGen. The processed data, model checkpoints, and benchmark results are available at Zenodo: https://doi.org/10.5281/zenodo.20594128.",
  "abstract_shown": true,
  "affiliations": [
    "University of Science and Technology of China School of Computer Science and Technology, , Hefei, 230027,",
    "Zhongguancun Academy , Beijing, 100094,",
    "Global Health Drug Discovery Institute , Beijing, 100192,"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 11,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Kehan Wu"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Wei Zhang",
      "orcid": "0009-0000-8088-0527"
    },
    {
      "affs": [
        2
      ],
      "name": "Han Guo"
    },
    {
      "affs": [
        2
      ],
      "name": "Renhe Liu"
    },
    {
      "affs": [
        1
      ],
      "name": "Yu Shi"
    },
    {
      "affs": [
        1
      ],
      "name": "Peiran Jin"
    },
    {
      "affs": [
        1
      ],
      "name": "Haiguang Liu"
    },
    {
      "affs": [
        0
      ],
      "name": "Enhong Chen"
    },
    {
      "affs": [
        1
      ],
      "name": "Tao Qin"
    },
    {
      "affs": [
        1
      ],
      "name": "Zequn Liu"
    },
    {
      "affs": [
        1
      ],
      "name": "Yingce Xia"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ac6gcd9zcck2v.bib",
  "cited_by": 0,
  "doi": "10.1093/bioinformatics/btag766",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13715970/",
  "id": "ac6gcd9zcck2v",
  "issns": [
    "1367-4803",
    "1367-4811"
  ],
  "landing_url": "https://academic.oup.com/bioinformatics/article/doi/10.1093/bioinformatics/btag766/8911832",
  "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": "W7221674645",
  "pdf_url": "https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btag766/71892478/btag766.pdf",
  "pmcid": "PMC13715970",
  "pmid": "42865030",
  "published": "2026-10-12",
  "publisher": "Oxford University Press (OUP)",
  "ris": "https://research.draconitic.com/paper/ac6gcd9zcck2v.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:38Z",
      "native_id": "10.1093/bioinformatics/btag766",
      "source": "crossref",
      "url": "https://academic.oup.com/bioinformatics/article/doi/10.1093/bioinformatics/btag766/8911832"
    },
    {
      "first_seen": "2026-11-01T12:23:08Z",
      "native_id": "PMC13715970",
      "source": "pmc",
      "url": "https://doi.org/10.1093/bioinformatics/btag766"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "issn:1367-4803",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Polypharmacological target-aware molecule generation",
  "title_text": "Polypharmacological target-aware molecule generation",
  "type": "peer-reviewed",
  "updated_at": "2026-11-01T12:23:08Z",
  "url": "https://research.draconitic.com/paper/ac6gcd9zcck2v",
  "venue": "Bioinformatics",
  "versions": []
}