{
  "abstract": "Therapeutic sutures can localize bioactive payloads to wounds, but burst release and insufficient coating durability can compromise regeneration and increase infection risk. In this work, we introduce a data-driven discovery strategy for bioresorbable polymer suture coatings that improve therapeutic retention and release kinetics. We develop GenPoly, an end-to-end multimodal generative framework that learns a shared polymer representation over paired polymer string sequences and molecular graphs via contrastive alignment, and couples this representation to property prediction, synthetic accessibility filtering, and motif-based candidate clustering to discover coating families beyond commonly used linear aliphatic polyesters. Starting from a clinically prevalent baseline, GenPoly identifies candidate coatings and selects representative polymers for experimental evaluation on nanoparticle-loaded sutures with recombinant human EGF to an in vitro human keratinocyte wound model. A discovered polymer coating of the LA-TMC family shows improved payload retention and sustained release, exhibiting a $3.3\\times$ increase in rhEGF concentration, accompanied by more localized Ki-67-positive keratinocyte proliferation at the wound bed, in comparison with the current state-of-the-art candidates. These findings suggest new opportunities in AI-driven polymer discovery, and highlight the viability of GenPoly in engineering bioresorbable coatings for suture-based therapeutic delivery in targeted tissue regeneration.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.13993",
  "author_total": 1,
  "authors": [
    {
      "name": "Raghav Ramji"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/d2d4cz89z5fwb.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.13993",
  "fulltext_url": null,
  "id": "d2d4cz89z5fwb",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.13993",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "q-bio.QM"
    ],
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7221800135",
  "pdf_url": "https://arxiv.org/pdf/2610.13993",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/d2d4cz89z5fwb.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:21Z",
      "native_id": "2610.13993",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.13993"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.QM",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Discovery of Bioresorbable Polymer Suture Coatings for Controlled Tissue Regeneration with Multimodal Foundation Models",
  "title_text": "Discovery of Bioresorbable Polymer Suture Coatings for Controlled Tissue Regeneration with Multimodal Foundation Models",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:21Z",
  "url": "https://research.draconitic.com/paper/d2d4cz89z5fwb",
  "venue": "arXiv",
  "versions": []
}