{
  "abstract": "Background: Bone Morphogenetic Protein 4 (BMP4), a member of the transforming growth factor-beta (TGF-β) superfamily, plays pivotal roles in embryonic development, tissue homeostasis, and disease pathogenesis—including cancer, fibrosis, and fibrodysplasia ossificans progressiva (FOP). The rapid advancement of artificial intelligence (AI) and machine learning (ML) methodologies over the past 5 years has created unprecedented opportunities for understanding and therapeutically targeting BMP4 biology at multiple scales, from atomic-resolution structure prediction to population-level clinical prognostication.\n\nObjective: This review systematically examines the intersection of BMP4 biology and AI across three core domains: (1) AI-driven drug discovery targeting BMP4 signaling pathways, (2) deep learning-based protein structure prediction and computational modeling of BMP4 and its receptor complexes, and (3) clinical AI applications for BMP4-associated diseases.\n\nMethods: A narrative review with systematic search elements was conducted across PubMed, Scopus, Web of Science, bioRxiv, and arXiv for English-language publications from 2020 to 2026, supplemented by targeted searches via Semantic Scholar. Included studies were categorized as BMP4-specific, BMP receptor/BMP-pathway-targeted, or transferable AI methodology not yet applied to BMP4.\n\nResults: We find that AI has accelerated BMP4-related research along multiple axes, though with important caveats. AlphaFold3 and related tools have generated high-confidence predicted models of BMP4-receptor complex architecture, aspects of which were previously intractable to experimental structure determination; these predictions remain to be fully validated experimentally. ML-guided virtual screening has identified first-in-class BMP pathway (receptor) modulators for FOP, although BMP4-specific small-molecule modulators have not yet been identified computationally. Single-cell transcriptomics combined with machine learning have revealed BMP4’s context-dependent roles in glioblastoma cell state transitions and cancer-associated fibroblast biology; and LASSO-Cox regression models incorporating BMP4 expression have shown promising prognostic value in pancreatic adenocarcinoma and gastric cancer, pending prospective validation. Significant gaps persist—most notably the absence of BMP4-specific de novo protein design efforts, the scarcity of large-scale training datasets for BMP4-targeted drug discovery, and the limited integration of multimodal AI approaches in BMP4 clinical research.\n\nConclusion: The convergence of BMP4 biology and AI represents a fertile research Frontier. While foundational tools are now in place, realizing the full translational potential of AI-augmented BMP4 research will require coordinated efforts in data generation, model development, and rigorous experimental and clinical validation. We outline priority directions for the next 5 years (approximately 2026–2031).",
  "abstract_shown": true,
  "affiliations": [
    "School of Traditional Chinese and Western Medicine, Gansu University of Chinese Medicine , , ,",
    "Wuhan University of Science and Technology , ,",
    "Tongji Medical College, Union Hospital, Huazhong University of Science and Technology , ,"
  ],
  "article_type": "review article",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Wanli Sun"
    },
    {
      "affs": [
        1
      ],
      "name": "Tongtong Huo"
    },
    {
      "affs": [
        2
      ],
      "name": "Zhewei Ye"
    },
    {
      "affs": [
        2
      ],
      "name": "Wu Wei"
    },
    {
      "affs": [
        0
      ],
      "name": "Yanqing Sun"
    },
    {
      "affs": [
        0
      ],
      "name": "Hui Cai"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/87nqwwmhbarxe.bib",
  "cited_by": 0,
  "doi": "10.3389/fmolb.2026.1930582",
  "fulltext_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13728478/?tool=EBI",
  "id": "87nqwwmhbarxe",
  "issns": [
    "2296-889X"
  ],
  "landing_url": "https://europepmc.org/article/PMC/PMC13728478",
  "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": [
    [
      "heading",
      "Review"
    ],
    [
      "openalex",
      "3002|Computational Drug Discovery Methods"
    ]
  ],
  "openalex": "W7223004174",
  "pdf_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13728478/pdf/?tool=EBI",
  "pmcid": "PMC13728478",
  "pmid": "42978922",
  "published": "2026-10-20",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/87nqwwmhbarxe.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T15:20:46Z",
      "native_id": "PMC:PMC13728478",
      "source": "europepmc",
      "url": "https://europepmc.org/article/PMC/PMC13728478"
    },
    {
      "first_seen": "2026-11-07T12:21:27Z",
      "native_id": "PMC13728478",
      "source": "pmc",
      "url": "https://doi.org/10.3389/fmolb.2026.1930582"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:3002|Computational Drug Discovery Methods",
      "subfield": "medicinal"
    }
  ],
  "title": "Bone morphogenetic protein 4 meets artificial intelligence: a comprehensive review of AI-driven advances in drug discovery, structural modeling, and clinical translation",
  "title_text": "Bone morphogenetic protein 4 meets artificial intelligence: a comprehensive review of AI-driven advances in drug discovery, structural modeling, and clinical translation",
  "type": "peer-reviewed",
  "updated_at": "2026-11-07T12:21:27Z",
  "url": "https://research.draconitic.com/paper/87nqwwmhbarxe",
  "venue": "Frontiers in molecular biosciences",
  "versions": []
}